CS109A Introduction to Data Science:

Lending Club Project¶

Harvard University
Fall 2018

#RUN THIS CELL 
import requests
from IPython.core.display import HTML
styles = requests.get("https://raw.githubusercontent.com/Harvard-IACS/2018-CS109A/master/content/styles/cs109.css").text
HTML(styles)

import numpy as np
import pandas as pd
import datetime
import warnings
warnings.filterwarnings('ignore')

import statsmodels.api as sm
from statsmodels.api import OLS

from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegressionCV
from sklearn.linear_model import LassoCV
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.preprocessing import PolynomialFeatures
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import KFold
from sklearn.preprocessing import MinMaxScaler
from sklearn.utils import resample

# Plotly visualizations
from plotly import tools
import plotly.plotly as py
import plotly.figure_factory as ff
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import tensorflow as tf

from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
from sklearn.pipeline import make_pipeline
from sklearn.datasets import make_blobs
import sklearn.metrics as metrics
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.linear_model import LogisticRegressionCV
from sklearn.model_selection import KFold
from sklearn.metrics import confusion_matrix

from sklearn.preprocessing import StandardScaler
import time

import math
from scipy.special import gamma

import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
%matplotlib inline

import seaborn as sns
sns.set()
import matplotlib.style
matplotlib.style.use('seaborn-whitegrid')
sns.set_style("white")
from IPython.display import display
init_notebook_mode(connected=True)

Overview

This notebook contains the following sections:¶

Part 1: Preparing the data
Part 2: Resample to achieve balanced classes
Part 3: Scale the data and generate train/test splits
Part 4 Determine significant predictors
Part 5: Exploring the data

Part 1: Preparing the data

This notebook uses the cleaned CSV data file data_cleaned_2016_2017.csv downloaded from https://drive.google.com/open?id=1LCk-dDFC7O_6ek1i0IIGqE07Rq-kf1Xz.

The cleaned dataset still needs some pre-processing in order to make it ready for modelling. This includes:

Removing several more columns that are not informative, for example where they duplicate other information or only have a single value
Drop the last digits of the zip code
Recoding some ordinal variables into numerical scales
Recoding some categorical variables into dummy variables

# increase some display options to display all columns and more rows.
pd.set_option('display.max_columns', None)
pd.options.display.max_rows = 150

# read in the 2016-2017 data set
original_df = pd.read_csv('../../../data/data_cleaned_2016_2017.csv', low_memory = False)

display(original_df.shape)
original_df.head()

(334109, 89)

We now use the pd.DataFrame.corr() function to find correlations between variables. A value of 1.0 means the variables are perfectly correlated and a value of 0 means they're not correlated at all. We decided on a threshold value of $> 0.9$.

correlations = original_df.corr()
threshold = 0.9

# display entire matrix and color in red if predictors > threshold
correlations.style.apply(lambda x: ["background: red" if v > threshold else "" for v in x], axis = 1)

Remove the following variables, since they all only have NaN values in every column, which doesn't seem very useful for anything.
- mo_sin_old_il_acct
- mo_sin_old_rev_tl_op
- mo_sin_rcnt_rev_tl_op
- mo_sin_rcnt_tl
- pmnt_plan

make_scatterplot (below) can be used to have a closer look at some of the variables that showed as highly correlated in the above analysis. Based on the correlation analysis, we therefore also decided to remove the following columns:
- funded_amnt
- funded_amnt_inv
- num_sats
- application_type
- num_actv_rev_tl

def make_scatterplot(x, y):
    '''Plots a scatter plot graph
    Inputs:
        x, y: pd.Series objects representing the x and y values to plot
    '''
    fig = plt.figure(figsize=(8,5))
    plt.scatter(x, y, alpha=0.8)
    plt.title('Compare two variables', fontsize=16)
    plt.xlabel(x.name, fontsize=14)
    plt.ylabel(y.name, fontsize=14)
    plt.legend(loc='best')
    plt.show()

Now we encode the ordinal and nominal categorical variables and drop the last two xx for zip_code:

object_columns = []
for column in original_df:
    if original_df[column].dtype == 'object':
        object_columns.append(column)
object_columns

['grade',
 'sub_grade',
 'home_ownership',
 'verification_status',
 'loan_status',
 'purpose',
 'zip_code',
 'addr_state']

# One function to do all of the above operations
def data_prep(df):
    '''Returns a cleaned up dataframe as basis for the EDA analysis
    Input:
        df: the pd.DataFrame object
    Returns:
        clean_df: pd.DataFrame object
    '''
    clean_df = df.copy()
    # Definitions
    cols_to_remove = ['mo_sin_old_il_acct', 'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op',
                      'mo_sin_rcnt_tl', 'funded_amnt', 'funded_amnt_inv', 'num_sats',
                   'application_type', 'num_actv_rev_tl', 'pymnt_plan']
    nominal_columns = ['home_ownership', 'verification_status', 'purpose', 'addr_state']
    prefixes = ['home', 'verify', 'purp', 'state']

    # Drop additional uninformative columns
    clean_df = clean_df.drop(columns=cols_to_remove)

    # strip xx from zip code
    clean_df["zip_code"] = [x.strip("xx") for x in clean_df["zip_code"].astype(str)]
    clean_df["zip_code"] = clean_df["zip_code"].astype(int)

    # exclude zip code from set of predictors for analysis
    # to be added back in later for checking discrimination...
    clean_df = clean_df.drop(columns='zip_code')


    # ordinal columns are encoded as numerical values, as there is an order
    clean_df["grade"].replace({"A": 1, "B": 2, "C": 3, "D": 4, "E": 5, "F": 6, "G": 7}, inplace = True)
    clean_df["sub_grade"].replace({"A1": 1, "A2": 2, "A3": 3, "A4": 4, "A5": 5,
                                "B1": 6, "B2": 7, "B3": 8, "B4": 9, "B5": 10,
                                "C1": 11, "C2": 12, "C3": 13, "C4": 14, "C5": 15,
                                "D1": 16, "D2": 17, "D3": 18, "D4": 19, "D5": 20,
                                "E1": 21, "E2": 22, "E3": 23, "E4": 24, "E5": 25,
                                "F1": 26, "F2": 27, "F3": 28, "F4": 29, "F5": 30,
                                "G1": 31, "G2": 32, "G3": 33, "G4": 34, "G5": 35}, inplace = True)

    # nominal columns are encoded via hot encoding by adding more columns
    clean_df = pd.get_dummies(clean_df, columns=nominal_columns, prefix=prefixes, drop_first=True)

    return clean_df

df_all = data_prep(original_df)

df_all.shape

(334109, 141)

Part 2: Resample to achieve balanced classes

Since we have imbalanced classes that can cause misleading assessment of model performance, we resample the classes here. We also take the opportunity to reduce the dataset size in the initial stages, so that different model specifications can be tested faster.

# Check balance of target values - it is unbalanced
df_all["loan_status"].value_counts().to_frame()

# Downsample and create balanced classes
def balance_classes(df, n_samples):
    # Define majority and minority classes, 1 = Fully paid, 0 = Charged off
    df_majority = df[df["loan_status"] == "Fully Paid"]
    df_minority = df[df["loan_status"] == "Charged Off"]

    # Downsample majority class
    df_majority_downsampled = resample(df_majority,
                                     replace = False,    # sample without replacement
                                     n_samples = n_samples,
                                     random_state = 1) # set random seed for reproducability

    # Downsample minority class
    df_minority_downsampled = resample(df_minority,
                                     replace = False,
                                     n_samples = n_samples,
                                     random_state = 1)

    # Recombine
    df_downsampled = pd.concat([df_majority_downsampled, df_minority_downsampled])

    return df_downsampled

# Define how many samples of each we want
n_samples = 10000

df = balance_classes(df_all, n_samples)

df["loan_status"].value_counts().to_frame()

display(df.describe())

Part 3: Scale the data and generate train/test splits

Since we will be using models that are sensitive to scale, we need to scale the data. This is done in a function for reproducability.

# Function to generate train test splits and scale the data
def get_train_test(df, test_size = 0.2):
    nonbinary_columns = ['id','loan_amnt','int_rate','installment','grade','sub_grade','emp_length',
                         'annual_inc','issue_d','dti', 'delinq_2yrs','earliest_cr_line','inq_last_6mths',
                         'open_acc','pub_rec','revol_bal','revol_util','total_acc', 'annual_inc_joint',
                         'dti_joint','acc_now_delinq','open_acc_6m','open_act_il', 'open_il_12m',
                         'open_il_24m', 'total_bal_il','il_util','open_rv_12m','open_rv_24m','max_bal_bc',
                         'all_util','inq_fi','total_cu_tl','inq_last_12m','acc_open_past_24mths',
                         'avg_cur_bal','bc_open_to_buy','bc_util','chargeoff_within_12_mths',
                         'delinq_amnt','mort_acc','num_accts_ever_120_pd', 'num_actv_bc_tl',
                         'num_bc_sats','num_bc_tl', 'num_il_tl', 'num_op_rev_tl', 'num_rev_accts',
                         'num_rev_tl_bal_gt_0', 'num_tl_120dpd_2m', 'num_tl_30dpd', 'num_tl_90g_dpd_24m',
                         'num_tl_op_past_12m', 'pct_tl_nvr_dlq', 'percent_bc_gt_75', 'pub_rec_bankruptcies',
                         'tax_liens', 'revol_bal_joint', 'sec_app_mort_acc', 'sec_app_revol_util',
                         'sec_app_mths_since_last_major_derog']
    result = df.copy()

    # Train test split
    data_train, data_test = train_test_split(result, test_size = test_size, random_state = 1)

    # Split into x and y
    X_train = data_train.iloc[:, data_train.columns != 'loan_status']
    y_train = data_train['loan_status']
    X_test = data_test.iloc[:, data_test.columns != 'loan_status']
    y_test = data_test['loan_status']

    # Incorporate scaling here so that we can use it consistently across all models
    scaler = StandardScaler()
    X_train[nonbinary_columns] = scaler.fit_transform(X_train[nonbinary_columns])
    X_test[nonbinary_columns] = scaler.transform(X_test[nonbinary_columns])

    return X_train, y_train, X_test, y_test

# Create scaled train and test sets
X_train, y_train, X_test, y_test = get_train_test(df)

/Users/rs/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:617: DataConversionWarning:

Data with input dtype int64, float64 were all converted to float64 by StandardScaler.

/Users/rs/anaconda3/lib/python3.6/site-packages/sklearn/base.py:462: DataConversionWarning:

Data with input dtype int64, float64 were all converted to float64 by StandardScaler.

/Users/rs/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:30: DataConversionWarning:

Data with input dtype int64, float64 were all converted to float64 by StandardScaler.

# Check that data has scaled correctly
display(X_train.describe())

Part 4: Determine significant predictors

We can now start to explore the data and check for "significant" predictors. First we create training and test sets and then we fit some models to see what important predictors would be according to those models. We also need to encode loan_status first, so LassoCV and DecisionTree won't throw an Exception.

y_train = y_train.replace({'Fully Paid': 1, 'Charged Off': 0})
y_test = y_test.replace({'Fully Paid': 1, 'Charged Off': 0})

alphas = (.1,.5,1,5,10,50,100)
fitted_lasso = LassoCV(alphas=alphas, max_iter=100000).fit(X_train, y_train)

print("Relevant variables according to variable selection via Lasso:\n")

result = {}

for index, val in enumerate(fitted_lasso.coef_):
    if fitted_lasso.coef_[index] != 0:
        result[fitted_lasso.coef_[index]] = X_train.columns[index]

for key in sorted(result.keys(), reverse=True) :
    print(key , ":" , result[key])

Relevant variables according to variable selection via Lasso:

0.0025568715312380545 : fico_range_low
9.484037990805908e-08 : fico_range_high
-0.027056759401023214 : sub_grade

Run another check using a Decision Tree and Random Forest and their feature importance to check which variables are important.

dec_tree = DecisionTreeClassifier(max_depth = 10).fit(X_train, y_train)
dec_feat_imp = dec_tree.feature_importances_
dec_feat_imp = 100.0 * (dec_feat_imp / dec_feat_imp.max())
index_sorted = np.argsort(dec_feat_imp)

print("Relevant variables according to variable selection via feature importance using Decision Tree:\n")

result = {}

for index in index_sorted:
    if dec_feat_imp[index] != 0:
        result[dec_feat_imp[index]] = X_train.columns[index]

for key in sorted(result.keys(), reverse=True) :
    print(key , ":" , result[key])

Relevant variables according to variable selection via feature importance using Decision Tree:

100.0 : int_rate
21.304675503227674 : sub_grade
19.197629186871147 : id
17.45445447222068 : dti
15.365088091616434 : avg_cur_bal
10.54705107784583 : revol_util
9.407515079333443 : annual_inc
8.783968761862633 : revol_bal
7.867514221028705 : loan_amnt
7.815662565909309 : bc_util
7.650440076626011 : mort_acc
7.609002056702982 : bc_open_to_buy
7.248263308732877 : installment
7.150234827997563 : num_op_rev_tl
6.7924055844896065 : max_bal_bc
6.395924569186223 : all_util
6.060403629983622 : emp_length
5.419391214879219 : total_bal_il
5.268225971175418 : open_acc
5.181095404879697 : num_rev_accts
4.892252407029087 : pct_tl_nvr_dlq
4.778186645963515 : acc_open_past_24mths
4.573886623238064 : percent_bc_gt_75
4.569155476705875 : earliest_cr_line
4.1197348447983515 : total_acc
4.118571405752972 : fico_range_low
3.883882601325824 : inq_fi
3.756030995318789 : num_actv_bc_tl
3.7184522097559527 : inq_last_12m
3.628998949035904 : fico_range_high
3.5083109537011423 : num_il_tl
3.282716799295942 : num_rev_tl_bal_gt_0
3.107980508341097 : open_il_24m
2.8951557435873747 : il_util
2.4852270134309595 : total_cu_tl
2.339773456316361 : num_bc_tl
2.3374372869119626 : home_RENT
2.309255882223218 : open_rv_24m
2.208079001367195 : num_tl_op_past_12m
2.0823173882060364 : mths_since_last_major_derog
1.9665788867124323 : num_bc_sats
1.8640989718936734 : open_act_il
1.861186957975004 : pub_rec
1.5372791893554898 : num_accts_ever_120_pd
1.4901598576319195 : state_NY
1.311623471612176 : home_OWN
1.0382387766755317 : pub_rec_bankruptcies
0.9579702494687025 : open_rv_12m
0.9122552493326146 : inq_last_6mths
0.8179578873795464 : state_TX
0.7789286312742919 : term
0.7764788426115419 : dti_joint
0.761403310712108 : open_acc_6m
0.7390311567904804 : tax_liens
0.7354112311201012 : state_PA
0.7190819637537914 : state_AZ
0.6455450118912908 : purp_other
0.633641018633876 : purp_home_improvement
0.6138191367547075 : state_NC
0.6090370662071478 : verify_Source Verified
0.6015087982320213 : mths_since_recent_inq
0.5997945975663626 : purp_debt_consolidation
0.5992010829529387 : open_il_12m
0.5739083027571913 : sec_app_revol_util
0.5661390062904715 : num_tl_90g_dpd_24m
0.5581995917558585 : annual_inc_joint
0.5544369655044484 : state_MN
0.5512910649082389 : state_GA
0.5303397827403792 : state_MD
0.5247161625208605 : verify_Verified
0.5032991762955196 : state_FL
0.49337843178168 : state_WA
0.4899693333063094 : state_IN
0.4868810157633434 : state_NH
0.39338839281963617 : state_LA
0.36730131376460196 : home_MORTGAGE
0.26255697131529426 : purp_credit_card
0.25354964380773254 : delinq_amnt
0.2490960244055073 : purp_major_purchase
0.23753598209355994 : delinq_2yrs
0.22062389999429233 : state_CT

# the accuracy score for Decision Tree
accuracy_score(dec_tree.predict(X_test), y_test)

0.608

# Check by running a basic random forest (without tuning parameters)
rf = RandomForestClassifier(n_estimators=int(X_train.shape[1]/2),
                            max_depth = 2).fit(X_train, y_train)
print("The precision score for the basic RF is:")
precision_score(rf.predict(X_test), y_test, pos_label = 1)

The precision score for the basic RF is:

0.5594132029339853

# Check that the top features make sense
rf_feat_imp = rf.feature_importances_
rf_feat_imp = 100.0 * (rf_feat_imp / rf_feat_imp.max())
sorted_idx = np.argsort(rf_feat_imp)
pos = np.arange(sorted_idx.shape[0]) + .5

#Plot
plt.figure(figsize=(10,24))
plt.barh(pos, rf_feat_imp[sorted_idx], align='center')
plt.yticks(pos, X_train.columns[sorted_idx])
plt.xlabel('Relative Importance')
plt.title('Variable Importance');

Summary:

From what the Lasso and the decision tree evaluation returned, below are the most interesting/significant variables. Those are the top 41, ranked by whether they appeared as important in Lasso, Decision tree and Random Forest. Since sub_grade is a more detailed view of grade, we've only included sub_grade.

int_rate
(Interest Rate on the loan)
sub_grade
(LC assigned loan subgrade) - we'll skip grade, since sub_grade correlates with grade and is more granular
avg_cur_bal
(Average current balance of all accounts)
dti
(A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income)
bc_open_to_buy
(Total open to buy on revolving bankcards)
fico_range_low
(The lower boundary range the borrower’s FICO at loan origination belongs to), we will skip fico_range_high as it correlates with the low range and doesn't provide additional information.
installment
(The monthly payment owed by the borrower if the loan originates)
emp_length
(Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years)
term
(The number of payments on the loan. Values are in months and can be either 36 or 60)
percent_bc_gt_75
(Percentage of all bankcard accounts > 75% of limit)
verification_status
(Indicates if income was verified by LC, not verified, or if the income source was verified)
revol_util
(Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit)
all_util
(Balance to credit limit on all trades)
home_ownership
(The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are: RENT, OWN, MORTGAGE, OTHER)
open_rv_24m
(Number of revolving trades opened in past 24 months)
num_rev_tl_bal_gt_0
(Number of revolving trades with balance >0)
loan_amnt
(The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value)
annual_inc
(The self-reported annual income provided by the borrower during registration)
mort_acc
(Number of mortgage accounts)
max_bal_bc
(Maximum current balance owed on all revolving accounts)
total_bal_il
(Total current balance of all installment accounts)
pct_tl_nvr_dlq
(Percent of trades never delinquent)
earliest_cr_line
(The month the borrower's earliest reported credit line was opened)
bc_util
(Ratio of total current balance to high credit/credit limit for all bankcard accounts)
revol_bal
(Total credit revolving balance)
total_acc
(The total number of credit lines currently in the borrower's credit file)
num_il_tl
(Number of installment accounts)
acc_open_past_24mths
(Number of trades opened in past 24 months)
il_util
(Ratio of total current balance to high credit/credit limit on all install acct)
delinq_amnt
(The past-due amount owed for the accounts on which the borrower is now delinquent)
num_actv_bc_tl
(Number of currently active bankcard accounts)
num_bc_sats
(Number of satisfactory bankcard accounts)
open_rv_12m
(Number of revolving trades opened in past 12 months)
num_rev_accts
(Number of revolving accounts)
open_acc
(The number of open credit lines in the borrower's credit file)
open_acc_6m
(Number of open trades in last 6 months)
open_act_il
(Number of currently active installment trades)
num_op_rev_tl
(Number of open revolving accounts)
total_cu_tl
(Number of finance trades)
inq_fi
(Number of personal finance inquiries)
inq_last_6mths
(The number of inquiries in past 6 months (excluding auto and mortgage inquiries))

Part 5: Exploring the data

# create dataframe with relevant predictors plus the response variable
eda_predictors = ['int_rate', 'sub_grade', 'avg_cur_bal', 'dti', 'bc_open_to_buy', 'fico_range_low', 'installment',
                  'emp_length', 'term', 'percent_bc_gt_75', 'verify_Verified', 'revol_util', 'all_util',
                  'home_MORTGAGE','home_RENT', 'open_rv_24m', 'num_rev_tl_bal_gt_0',
                  'loan_amnt', 'annual_inc', 'mort_acc', 'max_bal_bc', 'total_bal_il', 'pct_tl_nvr_dlq',
                  'earliest_cr_line', 'bc_util', 'revol_bal', 'total_acc', 'num_il_tl', 'acc_open_past_24mths',
                  'il_util', 'delinq_amnt', 'num_actv_bc_tl', 'num_bc_sats', 'open_rv_12m', 'num_rev_accts',
                  'open_acc','open_acc_6m', 'open_act_il', 'num_op_rev_tl',
                  'total_cu_tl', 'inq_fi', 'inq_last_6mths', 'loan_status']

# this reduced dataframe is created to be able to run a pairplot analysis
df_eda = df[eda_predictors]

df_eda.shape

(20000, 43)

sns.pairplot(df_eda.sample(1000), hue='loan_status');

Some general functions to use for displaying plots¶

def get_barplot(x,y):
    '''Function to display regular bar plots
    Inputs:
        x: pd.Series representing the x values
        y: pd.Series representing the y values
    '''
    title = y.name + ' over ' + x.name
    plt.figure(figsize=(14,8))
    plt.bar(x, y, align='center', alpha=0.6)
    plt.title(title, fontsize=16)
    plt.xlabel(x.name, fontsize=14)
    plt.ylabel(y.name, fontsize=14)
    plt.grid()
    plt.show()

def get_hbarplot(x,y):
    '''Function to display horizontal bar plots
    Inputs:
        x: pd.Series representing the x values
        y: pd.Series representing the y values
    '''
    title = y.name + ' over ' + x.name
    plt.figure(figsize=(10,14))
    plt.barh(x, y, align='center', alpha=0.6)
    plt.title(title, fontsize=16)
    plt.xlabel(y.name, fontsize=14)
    plt.ylabel(x.name, fontsize=14)
    plt.grid()
    plt.show()

def get_histogram(y,b):
    '''Function to display a histogram
    Inputs:
        y: pd.Series representing the values to display
        b: the number of bins to show
    '''
    title = 'Distribution of ' + y.name
    plt.figure(figsize=(14,8))
    plt.hist(y, bins=b)
    plt.title(title, fontsize=16)
    plt.xlabel(y.name, fontsize=14)
    plt.ylabel("Numbers of {}".format(y.name), fontsize=14)
    plt.grid()
    plt.show()

def get_violinplot(x, y, data):
    '''Function to display violin plots
    Inputs:
        x: a string used with data to represent the x axis values
        y: a string used to represent the y axis values
        data: the dataframe
    '''
    title = y + ' over ' + x
    x_category = np.sort(data[x].unique())

    fig, ax1 = plt.subplots(figsize=(15, 8), sharey=True)

    for idx, val in enumerate(x_category):
        parts = ax1.violinplot(data.loc[data[x]==val, y], positions=[idx],
                               showmeans=True, showmedians=True, showextrema=True)
        for pc in parts['bodies']:
            pc.set_facecolor('#D43F3A')
            pc.set_edgecolor('black')
            pc.set_alpha(.6)

        for partname in ('cbars','cmins','cmaxes', 'cmedians', 'cmeans'):
            vp = parts[partname]
            vp.set_edgecolor('black')
            vp.set_alpha(0.6)

    ax1.set_title(title, fontsize=16)
    ax1.set_xlabel(x, fontsize=14)
    ax1.set_ylabel(y, fontsize=14)
    ax1.set_xticks(np.arange(x_category.size))
    ax1.set_xticklabels(x_category)
    plt.show()

Analyze the distribution of the loan amounts¶

get_histogram(original_df['loan_amnt'], 40)

original_df['loan_amnt'].describe()

count    334109.000000
mean      14477.898306
std        9132.981886
min        1000.000000
25%        7200.000000
50%       12000.000000
75%       20000.000000
max       40000.000000
Name: loan_amnt, dtype: float64

We can see that the round \$5000 increment loan amounts (e.g. 10k, 15k, 20k, etc) are the most popular. Most loans are around $10000 and gradually decrease from there up to \$35k where they increase again slightly. 50% of all loans are below \\$12,000 and 75% below \$20,000.

Analyze the loan amount by sub grade¶

get_violinplot('sub_grade', 'loan_amnt', original_df)

We can see a trend that looks like smaller loans are usually taken borrower with higher ratings (grade) and higher loans are usually taken by borrowers with lower credit ratings.

Analyze loans issued and interest rates by states and sub grades¶

We can also show the number of loans, the average income and interest rate by state to see where most loans were made and under which conditions.

by_loan_amount = original_df.groupby(['addr_state'], as_index=False).loan_amnt.sum()
by_interest_rate = original_df.groupby(['addr_state'], as_index=False).int_rate.mean()
by_income = original_df.groupby(['addr_state'], as_index=False).annual_inc.mean()

states = by_loan_amount['addr_state'].values.tolist()
average_loan_amounts = by_loan_amount['loan_amnt'].values.tolist()
average_interest_rates = by_interest_rate['int_rate'].values.tolist()
average_annual_income = by_income['annual_inc'].values.tolist()

from collections import OrderedDict

metrics_data = OrderedDict([('state_codes', states),
                            ('issued_loans', average_loan_amounts),
                            ('interest_rate', average_interest_rates),
                            ('annual_income', average_annual_income)])

metrics_df = pd.DataFrame.from_dict(metrics_data)
metrics_df = metrics_df.round(decimals=2)
metrics_df.head()

data = metrics_df.sort_values(['issued_loans'])
get_hbarplot(data['state_codes'], data['issued_loans'])

data = metrics_df.sort_values(['annual_income'])
get_hbarplot(data['state_codes'], data['annual_income'])

high_loan_states = ['CA', 'TX', 'NY', 'FL']
highloans = metrics_df[metrics_df['state_codes'].isin(high_loan_states)]
display(highloans)

highloans_perc = (highloans['issued_loans'].sum() / metrics_df['issued_loans'].sum()) * 100
print("Percentage of loans made in states CA, TX, NY, FL relative to all loans: {:.2f}".format(highloans_perc))

Percentage of loans made in states CA, TX, NY, FL relative to all loans: 38.72

When looking at the data, we can see that the 4 states of CA, TX, NY and FL have issued almost 40% of all loans in 2016 and 2017.

When looking at the average annual incomes, 3 states (CA, NY and TX) are somewhere in the top 10 of states but FL is on rank. This leaves the question as to why there have been so many loans in FL?

It looks like the average interest rate is fairly evenly distributed across the states, roughly between 12,5 to 14,5% (see below).

data = metrics_df.sort_values(['interest_rate'])
get_hbarplot(data['state_codes'], data['interest_rate'])

Interest Rate by Sub Grade¶

group1 = original_df.groupby(['sub_grade'], as_index=False).int_rate.mean()
group1.head()

get_barplot(group1['sub_grade'], group1['int_rate'])

We see what we would've expected, that interest rates go up, as the loan sub grades decrease.

Analyze loan default rates¶

defaulted loans based on interest rates¶

original_df['int_round'] = original_df['int_rate'].round(0).astype(int)
df_y = original_df[['int_round','loan_status']]
crosst_y = pd.crosstab(original_df['int_round'], original_df['loan_status'])
crosst_y['Fully Paid %'] = crosst_y['Fully Paid'] / (crosst_y['Fully Paid'] + crosst_y['Charged Off'])*100
crosst_y

crosst_y[['Charged Off','Fully Paid']].plot(kind='bar', stacked=True, figsize=(12,8))
ax2 = plt.twinx()
sns.lineplot(data=crosst_y['Fully Paid %'], color="b", ax=ax2)
plt.title('Fully Paid and Charged Off loans by interest rate')
plt.show()

It seems like the interest rate is a reliable predictor of which loans might default in the future. Higher interest rate loans are more likely to default.

defaulted loans based on loan purpose and loan grades¶

cm = sns.light_palette("red", as_cmap=True)
crosst = pd.crosstab(original_df['purpose'], original_df['loan_status'])
crosst['Charged Off %'] = crosst['Charged Off'] / (crosst['Fully Paid'] + crosst['Charged Off'])*100
crosst.style.background_gradient(cmap = cm)

From the original data set we know our "base charged off rate", meaning the % of all charged off loans over all loans made in 2016-2017 is 23.64%. This means all loan purposes that show a higher percentage, are at a higher risk than average to default. In this case that is dept_collection, medical, moving, renewable_energy, small_business and other.

loan_grade = ['loan_status', 'grade']
cm = sns.light_palette("red", as_cmap=True)
cross_u = pd.crosstab(df[loan_grade[0]], original_df[loan_grade[1]], normalize='columns').style.background_gradient(cmap = cm)
cross_u

We see above that loans with grades of D and lower are more likely to lead to a default in the future than higher rated loans with for instance grades A or B.

defaulted loans based on state¶

fig = plt.figure(figsize=(18,10))
original_df[original_df['loan_status']=="Charged Off"].groupby('addr_state')['loan_status'].count().sort_values().plot(kind='barh')
plt.ylabel('State',fontsize=15)
plt.xlabel('Number of loans',fontsize=15)
plt.title('Number of defaulted loans per state',fontsize=20);

The states of CA, NY, TX and FL have the highest default rates but that is simply because they have the highest number of loans in general. If we wanted to get a more accurate default rate by state, we may have to calculate the defaulted loans relative to the number of loans issued in that state and then compare that to the national default average of 23.64%. The table below shows the states with a higher risk of default compared to the national average.

average = 23.64

crosst = pd.crosstab(original_df['addr_state'], original_df['loan_status'])
crosst['Charged Off %'] = crosst['Charged Off'] / (crosst['Fully Paid'] + crosst['Charged Off'])*100
crosst.sort_values('Charged Off %', ascending=False)[crosst['Charged Off %'] > average]

/Users/rs/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: UserWarning:

Boolean Series key will be reindexed to match DataFrame index.

	id	loan_amnt	funded_amnt	funded_amnt_inv	term	int_rate	installment	emp_length	annual_inc	issue_d	pymnt_plan	dti	delinq_2yrs	earliest_cr_line	fico_range_low	fico_range_high	inq_last_6mths	mths_since_last_delinq	mths_since_last_record	open_acc	pub_rec	revol_bal	revol_util	total_acc	initial_list_status	mths_since_last_major_derog	application_type	annual_inc_joint	dti_joint	acc_now_delinq	open_acc_6m	open_act_il	open_il_12m	open_il_24m	mths_since_rcnt_il	total_bal_il	il_util	open_rv_12m	open_rv_24m	max_bal_bc	all_util	inq_fi	total_cu_tl	inq_last_12m	acc_open_past_24mths	avg_cur_bal	bc_open_to_buy	bc_util	chargeoff_within_12_mths	delinq_amnt	mo_sin_old_il_acct	mo_sin_old_rev_tl_op	mo_sin_rcnt_rev_tl_op	mo_sin_rcnt_tl	mort_acc	mths_since_recent_bc	mths_since_recent_bc_dlq	mths_since_recent_inq	mths_since_recent_revol_delinq	num_accts_ever_120_pd	num_actv_bc_tl	num_actv_rev_tl	num_bc_sats	num_bc_tl	num_il_tl	num_op_rev_tl	num_rev_accts	num_rev_tl_bal_gt_0	num_sats	num_tl_120dpd_2m	num_tl_30dpd	num_tl_90g_dpd_24m	num_tl_op_past_12m	pct_tl_nvr_dlq	percent_bc_gt_75	pub_rec_bankruptcies	tax_liens	revol_bal_joint	sec_app_mort_acc	sec_app_revol_util	sec_app_mths_since_last_major_derog
id	1	-0.0456292	-0.0456292	-0.045516	0.00510278	0.0848256	-0.0383872	-0.0169376	0.00244029	0.833589	nan	-0.0169884	-0.0127991	0.0845452	0.068197	0.0681967	-0.00241664	-0.0189844	-0.00713965	-0.0315555	-0.0184074	-0.0311083	-0.0859639	-0.0401273	-0.039018	-0.016988	0.146709	0.138757	0.138557	-0.0130889	-0.0192934	-0.00926293	-0.0218833	0.00589419	-0.00312323	0.0045272	-0.0244249	-0.0139552	-0.0177426	-0.0219051	-0.0811412	0.0338149	0.00451937	-0.00235927	-0.00409831	0.027256	0.0610435	-0.091366	-0.000865381	-0.00363466	nan	nan	nan	nan	-0.0277226	-0.0151451	-0.0203849	0.00170865	-0.0187132	-0.00189675	-0.0346523	-0.0549845	-0.0103156	-0.0429805	-0.00498769	-0.0274384	-0.0466677	-0.0602487	-0.0309258	-0.00595114	-0.0102585	-0.00561178	-0.0179269	-0.00156829	-0.0760892	0.00648718	-0.0176822	0.173537	0.150042	0.201232	0.134191
loan_amnt	-0.0456292	1	1	0.999993	0.371943	0.171388	0.955203	0.100803	0.290591	-0.0223079	nan	0.0354099	-0.00144835	-0.138749	0.0734488	0.0734488	-0.00949889	-0.00402864	-0.067105	0.17634	-0.0470971	0.300435	0.119665	0.190422	0.0639881	-0.0380772	0.10254	0.128165	0.0989231	0.0017271	-0.0153681	0.0304857	0.00200697	0.0315075	0.0499328	0.157087	-0.0383439	-0.034697	-0.017261	0.353818	0.0146875	0.00764786	0.0695528	0.0164975	0.019626	0.215998	0.188939	0.0761079	-0.00195514	0.00276253	nan	nan	nan	nan	0.212504	0.0462264	-0.0068254	0.0138877	-0.00389924	-0.043254	0.199607	0.161251	0.220138	0.199425	0.0729088	0.160412	0.163052	0.157129	0.176213	0.0013363	0.00312459	-0.0185283	-0.012524	0.0702641	0.04447	-0.0722752	0.0132666	0.0917166	0.0654644	0.0651616	0.025622
funded_amnt	-0.0456292	1	1	0.999993	0.371943	0.171388	0.955203	0.100803	0.290591	-0.0223079	nan	0.0354099	-0.00144835	-0.138749	0.0734488	0.0734488	-0.00949889	-0.00402864	-0.067105	0.17634	-0.0470971	0.300435	0.119665	0.190422	0.0639881	-0.0380772	0.10254	0.128165	0.0989231	0.0017271	-0.0153681	0.0304857	0.00200697	0.0315075	0.0499328	0.157087	-0.0383439	-0.034697	-0.017261	0.353818	0.0146875	0.00764786	0.0695528	0.0164975	0.019626	0.215998	0.188939	0.0761079	-0.00195514	0.00276253	nan	nan	nan	nan	0.212504	0.0462264	-0.0068254	0.0138877	-0.00389924	-0.043254	0.199607	0.161251	0.220138	0.199425	0.0729088	0.160412	0.163052	0.157129	0.176213	0.0013363	0.00312459	-0.0185283	-0.012524	0.0702641	0.04447	-0.0722752	0.0132666	0.0917166	0.0654644	0.0651616	0.025622
funded_amnt_inv	-0.045516	0.999993	0.999993	1	0.372176	0.171397	0.955109	0.100855	0.290609	-0.0221908	nan	0.0353379	-0.00149396	-0.138754	0.0735679	0.0735679	-0.0095706	-0.00407488	-0.0671567	0.176311	-0.0471432	0.300432	0.119642	0.190395	0.0647964	-0.0381205	0.102532	0.12816	0.0989095	0.00170044	-0.0154125	0.0304666	0.00198669	0.0314859	0.0499276	0.157077	-0.0383544	-0.0347373	-0.0173085	0.353833	0.0146675	0.00763789	0.0695417	0.0164552	0.0195828	0.216055	0.188967	0.0760853	-0.00196807	0.00274976	nan	nan	nan	nan	0.212543	0.0462363	-0.00685981	0.0138697	-0.00393152	-0.0432889	0.199572	0.161207	0.220116	0.199397	0.0728883	0.160384	0.163024	0.157086	0.176186	0.00131546	0.00310217	-0.018561	-0.0125626	0.0703008	0.0444573	-0.0723112	0.0132395	0.0917409	0.0654809	0.065188	0.0256408
term	0.00510278	0.371943	0.371943	0.372176	1	0.394674	0.14862	0.0600385	0.0547661	0.0123301	nan	0.0483536	-0.0106056	-0.0360779	0.00229987	0.00230004	0.00795103	-0.0100385	-0.0059825	0.0711056	-0.0108748	0.0722922	0.0578313	0.0880162	0.176625	-0.0140784	0.0642611	0.0643084	0.0637183	-0.00242646	0.0139953	0.0343187	0.0419519	0.0627368	0.0352736	0.0769668	0.042335	-0.0146462	-0.00613441	0.0903663	0.050348	0.027585	0.0538909	0.0303021	0.0368651	0.0757197	0.0209426	0.0446585	-0.00225203	0.000162738	nan	nan	nan	nan	0.0901313	0.0152259	-0.00936181	0.0248917	-0.0117617	-0.0171136	0.0527023	0.0559652	0.0602925	0.0500086	0.0653109	0.0507575	0.0501363	0.0528589	0.0707827	-0.00132722	-0.00163741	-0.0113272	0.0181777	0.0383303	0.0400646	-0.0025567	-0.00782063	0.0546446	0.0462316	0.0512744	0.0269252
int_rate	0.0848256	0.171388	0.171388	0.171397	0.394674	1	0.214653	-0.0163869	-0.0710463	0.059859	nan	0.158991	0.0322546	0.119122	-0.358621	-0.358618	0.199015	0.0460827	0.0646569	0.0110733	0.0548128	-0.0107056	0.212328	-0.0444001	-0.162455	0.0596923	0.0411864	0.0263215	0.0560887	0.0065543	0.170999	0.0333187	0.19403	0.171914	-0.0102362	0.0376085	0.125128	0.150425	0.158809	-0.0424092	0.25345	0.145232	0.0204437	0.1967	0.208535	-0.0883193	-0.244328	0.210012	0.00647974	0.00431558	nan	nan	nan	nan	-0.111063	-0.0289126	0.0239265	0.095931	0.0266176	0.0362125	0.0534045	0.0947076	-0.0243449	-0.0745048	0.0154559	0.00648551	-0.0537557	0.0955494	0.0108543	0.00235276	0.00567208	0.0198795	0.214301	-0.0472754	0.205924	0.0569179	0.0151899	0.0305633	0.00964099	0.0466987	0.0322549
installment	-0.0383872	0.955203	0.955203	0.955109	0.14862	0.214653	1	0.0877734	0.272842	-0.0204832	nan	0.0473371	0.0060309	-0.115743	0.0210835	0.0210838	0.0163271	0.00647653	-0.0553965	0.167188	-0.0355255	0.28601	0.139345	0.16772	-0.00837457	-0.0264476	0.0913125	0.113794	0.0907054	0.00338448	0.00464014	0.0273263	0.0181085	0.0411935	0.0420945	0.146332	-0.0330059	-0.00946319	0.00898484	0.331862	0.0367535	0.0209666	0.0597467	0.037195	0.0424672	0.184681	0.150411	0.098197	-0.000521834	0.00357904	nan	nan	nan	nan	0.175254	0.0421676	-0.000387367	0.0207774	0.00415115	-0.0346932	0.20317	0.168627	0.209793	0.184321	0.0598414	0.156451	0.149827	0.165577	0.16713	0.00192625	0.00431939	-0.0139407	0.0132499	0.0553822	0.0651471	-0.0633205	0.018998	0.0794495	0.0522894	0.0577813	0.0232078
emp_length	-0.0169376	0.100803	0.100803	0.100855	0.0600385	-0.0163869	0.0877734	1	0.0991428	-0.0165807	nan	-0.0122115	0.0192935	-0.115603	0.00891098	0.00891115	0.0031631	0.0456234	0.0160075	0.0668743	0.0123518	0.0870979	0.0509222	0.103768	0.0200416	0.0245972	-0.0747383	-0.0584512	-0.0694189	0.0108487	0.0226559	-0.0626386	0.0474697	0.0610861	0.0523735	-0.00350992	-0.019046	0.00905316	0.00405258	0.0713191	-0.0147168	0.0092254	0.0834455	0.000861288	0.038457	0.0955353	0.0212737	0.0420823	0.00673721	0.00203488	nan	nan	nan	nan	0.158505	0.00463817	0.0261163	0.00435118	0.030035	0.00493574	0.0758387	0.111782	0.0688841	0.0899515	0.00275573	0.0982971	0.11622	0.112049	0.0659711	0.00146513	0.0105402	-0.00294973	0.0352015	-0.0166497	0.0374231	0.00503536	0.00531877	-0.0640999	-0.0570702	-0.0825761	-0.0539475
annual_inc	0.00244029	0.290591	0.290591	0.290609	0.0547661	-0.0710463	0.272842	0.0991428	1	0.00654048	nan	-0.126809	0.0282487	-0.119002	0.058931	0.0589308	0.0292207	0.0405467	-0.0244687	0.126109	-0.00382472	0.272479	0.0446215	0.15851	0.0346802	0.0116384	-0.0536792	-0.0215331	-0.0542628	0.0128571	0.0373415	0.0633142	0.0797827	0.102467	0.0499242	0.201533	0.00228085	-0.00400232	-0.0103785	0.24945	0.0067374	0.053162	0.0419613	0.064537	0.0610748	0.287459	0.153362	0.0121136	0.00481134	0.00818791	nan	nan	nan	nan	0.198136	0.0176033	0.0295602	0.0316428	0.0280972	0.00959516	0.106956	0.0792329	0.123213	0.128081	0.0929922	0.0782448	0.0984909	0.077653	0.125925	0.00766741	0.0105974	0.00193351	0.0520409	0.000633011	0.000613847	-0.0367303	0.0350064	-0.0211773	-0.0262688	-0.0391232	-0.0272948
issue_d	0.833589	-0.0223079	-0.0223079	-0.0221908	0.0123301	0.059859	-0.0204832	-0.0165807	0.00654048	1	nan	-0.00674063	-0.0144732	0.0647172	0.0604769	0.0604766	-0.00617351	-0.0207553	-0.0081671	-0.0167096	-0.0157999	-0.0115209	-0.066062	-0.0251697	-0.0257494	-0.0190358	0.131558	0.123699	0.12324	-0.00929717	-0.0237732	-0.0104031	-0.0312628	-0.00431884	-0.00258569	0.0028623	-0.0295579	-0.0136302	-0.0139293	-0.0016616	-0.0746282	0.0260245	0.00432069	-0.00388903	-0.00624408	0.0272625	0.0629564	-0.0728878	0.00153744	-0.00383244	nan	nan	nan	nan	-0.0154189	-0.00796961	-0.0223056	0.000616289	-0.0197745	-0.00689764	-0.0152928	-0.032089	0.00675165	-0.0233043	-0.00598991	-0.00992501	-0.0268819	-0.0363046	-0.0163005	-0.00344101	-0.00769706	-0.00695552	-0.0211688	0.00721334	-0.0597402	0.0029982	-0.0132262	0.153795	0.135667	0.181264	0.120069
pymnt_plan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
dti	-0.0169884	0.0354099	0.0354099	0.0353379	0.0483536	0.158991	0.0473371	-0.0122115	-0.126809	-0.00674063	nan	1	-0.0104998	-0.0338418	-0.0540789	-0.0540823	0.00111515	-0.0170658	-0.0245811	0.195946	-0.0305627	0.103301	0.148754	0.160071	-0.0259364	-0.0273196	0.189708	0.167284	0.231641	-0.00105407	0.0302801	0.173504	0.131945	0.180339	0.11519	0.192938	0.165749	-0.000557731	0.0239507	0.0851506	0.170675	0.055609	0.09578	0.0308119	0.101628	-0.0724721	-0.063867	0.150757	-0.00259618	-0.00724449	nan	nan	nan	nan	-0.0128473	0.00830909	-0.0249932	0.0291981	-0.0196061	-0.0274789	0.126229	0.181384	0.0754692	0.0552756	0.157749	0.125151	0.097101	0.184929	0.195886	-0.00494301	0.001442	-0.0118116	0.0566104	0.0640695	0.133691	-0.0127161	-0.0257022	0.157884	0.12818	0.156272	0.0887798
delinq_2yrs	-0.0127991	-0.00144835	-0.00144835	-0.00149396	-0.0106056	0.0322546	0.0060309	0.0192935	0.0282487	-0.0144732	nan	-0.0104998	1	-0.0749717	-0.171801	-0.171798	0.0269384	0.344679	-0.0506505	0.0522946	-0.0366986	-0.0226474	-0.00458231	0.112875	-0.011731	0.226492	-0.00262258	0.00268839	-0.00373691	0.123617	-0.000239808	0.0640688	-0.00885671	-0.0258594	0.0277418	0.0485201	0.0141296	-0.0234147	-0.0489541	-0.0515156	0.0213504	0.0229035	0.0172532	0.0264162	-0.0585419	0.0317852	-0.061073	-0.00470567	0.152355	0.035054	nan	nan	nan	nan	0.0571907	-0.0158934	0.317727	0.0128847	0.352637	0.216559	-0.0328287	0.00268074	-0.026932	0.0308758	0.082474	0.0110338	0.0751926	-0.00182585	0.049985	0.0472085	0.103201	0.665355	-0.0285821	-0.440008	-0.00673088	-0.064268	0.00593368	-0.00670418	-0.00149503	-0.00247188	0.0117696
earliest_cr_line	0.0845452	-0.138749	-0.138749	-0.138754	-0.0360779	0.119122	-0.115743	-0.115603	-0.119002	0.0647172	nan	-0.0338418	-0.0749717	1	-0.090225	-0.0902306	-0.00866513	-0.132666	-0.0682431	-0.137432	-0.059932	-0.200211	-0.0336224	-0.265802	-0.0406771	-0.112574	0.0119714	-0.00187587	0.00987256	-0.0314438	-0.0118584	0.0583278	-0.00247454	0.00875091	-0.036872	-0.0285505	0.0601059	0.00055375	0.024354	-0.190607	0.0536393	0.0292811	-0.0403152	0.0223407	0.021144	-0.118321	-0.140977	-0.0234079	-0.0266618	-0.00867312	nan	nan	nan	nan	-0.27796	-0.00855518	-0.120467	0.019383	-0.132552	-0.0854754	-0.116417	-0.15247	-0.13836	-0.263238	-0.0181363	-0.169198	-0.307887	-0.149252	-0.133176	-0.0106247	-0.0273597	-0.0329759	-0.00379716	0.0884751	-0.0353742	-0.0560401	-0.0281559	-0.00784146	-0.0099418	0.0119592	0.0037996
fico_range_low	0.068197	0.0734488	0.0734488	0.0735679	0.00229987	-0.358621	0.0210835	0.00891098	0.058931	0.0604769	nan	-0.0540789	-0.171801	-0.090225	1	1	-0.097505	-0.307014	-0.243953	0.0196743	-0.20027	-0.000530307	-0.465726	0.0252076	0.0770303	-0.282168	0.0178947	0.0257033	0.010836	-0.0428563	-0.0616149	-0.0134152	-0.0216399	-0.017437	-0.00255944	0.0117294	-0.0818219	-0.109483	-0.133455	0.0460722	-0.416169	-0.0815677	-0.00267085	-0.126787	-0.10921	0.10444	0.507928	-0.470744	-0.0528395	-0.0151574	nan	nan	nan	nan	0.104666	0.00784537	-0.202268	-0.094217	-0.236348	-0.194331	-0.120806	-0.19519	0.0642446	0.0737801	-0.0171835	0.0134573	0.0295203	-0.195455	0.0240883	-0.0180953	-0.0321727	-0.101024	-0.0953209	0.296871	-0.399495	-0.206976	-0.0617305	0.0357951	0.0349613	0.0193274	0.00648239
fico_range_high	0.0681967	0.0734488	0.0734488	0.0735679	0.00230004	-0.358618	0.0210838	0.00891115	0.0589308	0.0604766	nan	-0.0540823	-0.171798	-0.0902306	1	1	-0.0975053	-0.307012	-0.243949	0.0196713	-0.200267	-0.000532927	-0.465723	0.0252047	0.0770285	-0.282164	0.0178956	0.025704	0.0108358	-0.0428545	-0.0616173	-0.0134182	-0.0216427	-0.0174411	-0.00256083	0.0117265	-0.0818255	-0.109485	-0.133456	0.0460666	-0.41617	-0.0815686	-0.00267086	-0.126788	-0.109214	0.104442	0.507929	-0.470741	-0.052837	-0.0151572	nan	nan	nan	nan	0.104667	0.00784578	-0.202266	-0.0942215	-0.236346	-0.194328	-0.120807	-0.19519	0.0642422	0.0737779	-0.0171868	0.0134556	0.0295188	-0.195455	0.0240852	-0.018095	-0.0321722	-0.101022	-0.0953241	0.296867	-0.399491	-0.206973	-0.0617298	0.0357942	0.0349624	0.0193286	0.00648387
inq_last_6mths	-0.00241664	-0.00949889	-0.00949889	-0.0095706	0.00795103	0.199015	0.0163271	0.0031631	0.0292207	-0.00617351	nan	0.00111515	0.0269384	-0.00866513	-0.097505	-0.0975053	1	0.0402042	0.0876163	0.167398	0.0750018	-0.00286105	-0.102894	0.160403	-0.0554694	0.0821566	-0.0223655	-0.0208437	-0.0196721	-0.00414494	0.424702	0.0306383	0.145311	0.117973	0.0198792	0.0504319	0.0717362	0.341016	0.298656	-0.0542851	-0.048355	0.218088	0.0313672	0.484791	0.303038	-0.0440917	0.0318196	-0.0897593	0.0102862	-0.00224899	nan	nan	nan	nan	0.0126668	0.0173994	0.0349001	0.209879	0.0338875	0.0462168	0.101559	0.156783	0.15623	0.141211	0.0668369	0.175681	0.172687	0.129672	0.163325	-0.00281417	-0.00666443	0.0277039	0.353596	-0.022165	-0.0854127	0.0855617	0.0181919	-0.0193547	-0.0154953	-0.021927	-0.00925314
mths_since_last_delinq	-0.0189844	-0.00402864	-0.00402864	-0.00407488	-0.0100385	0.0460827	0.00647653	0.0456234	0.0405467	-0.0207553	nan	-0.0170658	0.344679	-0.132666	-0.307014	-0.307012	0.0402042	1	-0.0467039	0.0563469	-0.0189539	-0.0471993	0.0101965	0.164877	-0.00998736	0.570468	-0.00666193	-0.000829696	-0.0068887	0.0689108	0.0491603	0.0527387	0.0388924	0.0373714	0.0681927	0.0643094	0.0429723	0.0417107	0.0353053	-0.0817066	0.053505	0.073993	0.0386346	0.074587	0.047586	0.0490007	-0.111267	0.0182409	0.0702054	0.0207741	nan	nan	nan	nan	0.107099	-0.0147326	0.550657	0.0537591	0.707576	0.362446	-0.0361648	0.0130495	-0.0490637	0.0499351	0.120277	0.0219983	0.103071	0.0165125	0.0554331	0.0287721	0.0562023	0.152675	0.0539345	-0.608835	0.0043547	-0.0888289	0.0265877	-0.0151579	-0.00237543	-0.00673003	0.0257454
mths_since_last_record	-0.00713965	-0.067105	-0.067105	-0.0671567	-0.0059825	0.0646569	-0.0553965	0.0160075	-0.0244687	-0.0081671	nan	-0.0245811	-0.0506505	-0.0682431	-0.243953	-0.243949	0.0876163	-0.0467039	1	-0.0119334	0.796918	-0.11151	-0.0671207	0.010897	-0.0179055	-0.000451319	-0.000572396	-0.00698251	-0.00275022	-0.0054401	0.0661928	-0.0315764	0.0486684	0.0540555	0.0218196	-0.0264372	0.0319805	0.093185	0.122713	-0.144157	-0.0136064	0.0762482	0.00847277	0.105516	0.123906	-0.0736874	-0.113859	-0.0168851	-0.011433	0.000426055	nan	nan	nan	nan	-0.0264072	0.00549048	-0.05204	0.0695888	-0.0663142	-0.0108138	-0.0466023	0.00624367	-0.0602441	-0.0142455	-0.0135545	0.0195522	0.035464	0.00290667	-0.0173759	0.00103694	-0.00998001	-0.0169895	0.0987125	0.0368363	-0.0300964	0.776127	0.299501	-0.0158114	-0.00506234	-0.00774973	-0.0118531
open_acc	-0.0315555	0.17634	0.17634	0.176311	0.0711056	0.0110733	0.167188	0.0668743	0.126109	-0.0167096	nan	0.195946	0.0522946	-0.137432	0.0196743	0.0196713	0.167398	0.0563469	-0.0119334	1	-0.015478	0.21703	-0.135009	0.711668	0.0152771	0.0159066	-0.0184166	-0.000557158	-0.000784209	0.0224183	0.281506	0.51498	0.167873	0.237871	0.108818	0.342034	0.21282	0.369619	0.473997	0.106785	-0.0123935	0.108395	0.101001	0.184489	0.51123	-0.124814	0.305943	-0.0741913	0.005978	0.00331224	nan	nan	nan	nan	0.128376	0.0907996	0.0272822	0.128645	0.0595541	0.0314553	0.551326	0.661589	0.6326	0.5457	0.38191	0.840337	0.671288	0.663984	0.998662	0.00466632	0.0196159	0.0159108	0.391381	0.100041	-0.0750634	-0.0134098	-0.0073593	0.0107744	-0.0037312	-0.0234721	-0.0122987
pub_rec	-0.0184074	-0.0470971	-0.0470971	-0.0471432	-0.0108748	0.0548128	-0.0355255	0.0123518	-0.00382472	-0.0157999	nan	-0.0305627	-0.0366986	-0.059932	-0.20027	-0.200267	0.0750018	-0.0189539	0.796918	-0.015478	1	-0.0872123	-0.0517418	-0.00591755	-0.0174667	0.0148058	-0.00585126	-0.00959029	-0.00793729	-0.00257833	0.0568102	-0.028641	0.0374344	0.0381032	0.0166762	-0.0223009	0.0232084	0.0807145	0.103701	-0.114796	-0.00883367	0.0674735	-0.00157323	0.0904722	0.0998176	-0.0579094	-0.0907413	-0.0153993	-0.00895587	0.00281559	nan	nan	nan	nan	-0.0255759	0.005651	-0.0322887	0.0573158	-0.0431191	0.00321878	-0.029659	0.00564454	-0.044667	-0.0180016	-0.0178215	0.0107301	0.0135757	0.00396216	-0.019072	0.00475399	-0.00658831	-0.0117064	0.0823809	0.0121686	-0.0295045	0.654163	0.686977	-0.0161148	-0.0083866	-0.0103497	-0.0104793
revol_bal	-0.0311083	0.300435	0.300435	0.300432	0.0722922	-0.0107056	0.28601	0.0870979	0.272479	-0.0115209	nan	0.103301	-0.0226474	-0.200211	-0.000530307	-0.000532927	-0.00286105	-0.0471993	-0.11151	0.21703	-0.0872123	1	0.262357	0.185866	0.0219457	-0.0746145	-0.00300515	0.0172646	0.00648542	0.00352412	-0.0193387	-0.0012892	-0.0352578	-0.0244266	0.0215199	0.0925043	-0.0592588	-0.00142028	0.00277935	0.546885	0.109251	-0.0468321	0.0357439	-0.0253142	-0.00365459	0.281683	0.150496	0.191461	-0.00967216	0.00223265	nan	nan	nan	nan	0.216294	0.0414679	-0.0326004	-0.00693511	-0.0274949	-0.070373	0.309452	0.309683	0.279112	0.232235	0.0125949	0.226513	0.215664	0.308051	0.215405	-0.00285345	0.0069155	-0.028822	-0.0157294	0.0965034	0.157006	-0.107666	-0.010275	0.0350645	0.00766717	0.00317158	-0.0122944
revol_util	-0.0859639	0.119665	0.119665	0.119642	0.0578313	0.212328	0.139345	0.0509222	0.0446215	-0.066062	nan	0.148754	-0.00458231	-0.0336224	-0.465726	-0.465723	-0.102894	0.0101965	-0.0671207	-0.135009	-0.0517418	0.262357	1	-0.108596	-0.0264031	0.00484744	0.0246415	0.0259453	0.0342257	-0.0303393	-0.181813	0.0458067	-0.0838585	-0.0664247	0.01289	0.0328792	-0.00230849	-0.197121	-0.215097	0.3258	0.65511	-0.0734558	0.0355184	-0.123023	-0.209715	0.142375	-0.464889	0.841099	-0.0120491	-0.00763357	nan	nan	nan	nan	0.0337002	0.0104042	-0.00121411	-0.063428	-0.00476487	-0.0197588	0.114271	0.122157	-0.11625	-0.150926	0.0158534	-0.201499	-0.181344	0.130536	-0.135513	-0.0146131	-0.0215178	-0.00928085	-0.206963	-0.0403029	0.723753	-0.0690148	-0.00643908	0.0198315	0.00492423	0.0295645	-0.00544781
total_acc	-0.0401273	0.190422	0.190422	0.190395	0.0880162	-0.0444001	0.16772	0.103768	0.15851	-0.0251697	nan	0.160071	0.112875	-0.265802	0.0252076	0.0252047	0.160403	0.164877	0.010897	0.711668	-0.00591755	0.185866	-0.108596	1	0.0340599	0.12161	-0.0146581	0.00482136	0.00074875	0.0288889	0.258755	0.373834	0.254223	0.348491	0.16105	0.407291	0.185937	0.257423	0.321715	0.121812	-0.00023709	0.16573	0.29089	0.213623	0.455349	0.0360446	0.244732	-0.0744188	0.0361676	0.0043422	nan	nan	nan	nan	0.347698	0.0360243	0.126063	0.132395	0.153044	0.145121	0.304611	0.404457	0.412615	0.621164	0.69256	0.577508	0.761235	0.405051	0.708356	0.00882538	0.0229275	0.0656149	0.354305	0.0289515	-0.0646244	0.0234767	-0.0252072	0.0101585	0.0147019	-0.020617	-0.0080504
initial_list_status	-0.039018	0.0639881	0.0639881	0.0647964	0.176625	-0.162455	-0.00837457	0.0200416	0.0346802	-0.0257494	nan	-0.0259364	-0.011731	-0.0406771	0.0770303	0.0770285	-0.0554694	-0.00998736	-0.0179055	0.0152771	-0.0174667	0.0219457	-0.0264031	0.0340599	1	-0.0163854	0.0156404	0.0196043	0.0126395	-0.000441343	-0.0436611	0.00142055	-0.0390542	-0.0274876	0.0124691	0.0131395	-0.0197974	-0.0463635	-0.044386	0.0388421	-0.0430698	-0.0301753	0.0112963	-0.0453963	-0.0452869	0.0415878	0.0553371	-0.028589	-0.000363524	-0.00318297	nan	nan	nan	nan	0.0511063	0.0128764	-0.00611035	-0.0138002	-0.00568527	-0.0122598	0.00199673	-0.00581582	0.0205778	0.0304233	0.0147074	0.0106175	0.0249612	-0.00742326	0.0153477	0.000884112	-0.000503387	-0.0117098	-0.053438	0.0182647	-0.0279433	-0.0149067	-0.00594051	0.0176648	0.0160926	0.0132245	0.00580819
mths_since_last_major_derog	-0.016988	-0.0380772	-0.0380772	-0.0381205	-0.0140784	0.0596923	-0.0264476	0.0245972	0.0116384	-0.0190358	nan	-0.0273196	0.226492	-0.112574	-0.282168	-0.282164	0.0821566	0.570468	-0.000451319	0.0159066	0.0148058	-0.0746145	0.00484744	0.12161	-0.0163854	1	-0.0138163	-0.01033	-0.0145247	0.0355687	0.0797902	0.035763	0.0460466	0.0300871	0.049313	0.0439697	0.0624107	0.0929728	0.0899842	-0.115907	0.0576812	0.069348	0.00367465	0.0917672	0.0852758	0.0144104	-0.125023	0.0105724	0.122622	0.0333099	nan	nan	nan	nan	0.066741	-0.00798391	0.383217	0.0621868	0.40456	0.584653	-0.0278289	0.0208443	-0.038223	0.0409552	0.0925125	-0.00827835	0.0716812	0.00823319	0.0135839	0.0468141	-0.00173777	0.271122	0.097447	-0.559342	-0.00136059	-0.0395159	0.019823	-0.020143	-0.00844678	-0.0127033	0.0311284
application_type	0.146709	0.10254	0.10254	0.102532	0.0642611	0.0411864	0.0913125	-0.0747383	-0.0536792	0.131558	nan	0.189708	-0.00262258	0.0119714	0.0178947	0.0178956	-0.0223655	-0.00666193	-0.000572396	-0.0184166	-0.00585126	-0.00300515	0.0246415	-0.0146581	0.0156404	-0.0138163	1	0.900298	0.925836	-0.0022275	-0.0156001	-0.00278045	-0.013887	-0.0119551	-0.00150543	0.011471	-0.0107439	-0.0262963	-0.0262304	0.000584367	0.016174	0.0142427	0.0281037	0.00287337	-0.0214221	0.0427614	-0.0195873	0.019681	-0.00785714	-0.0028434	nan	nan	nan	nan	0.0332519	-0.0190638	-0.00984521	0.00122771	-0.00934307	-0.00791016	-0.0311423	-0.0208105	-0.0395794	-0.0383825	-0.00106958	-0.0229003	-0.0268364	-0.0160854	-0.0177416	-0.00323222	0.000418642	-0.00540271	-0.0241895	0.00370765	0.0252186	0.00528841	-0.00976738	0.572076	0.504644	0.674253	0.446625
annual_inc_joint	0.138757	0.128165	0.128165	0.12816	0.0643084	0.0263215	0.113794	-0.0584512	-0.0215331	0.123699	nan	0.167284	0.00268839	-0.00187587	0.0257033	0.025704	-0.0208437	-0.000829696	-0.00698251	-0.000557158	-0.00959029	0.0172646	0.0259453	0.00482136	0.0196043	-0.01033	0.900298	1	0.803658	-0.000826569	-0.0113082	0.00611445	-0.00859938	-0.00526274	0.00420784	0.0306911	-0.0107355	-0.0234255	-0.0238085	0.0234647	0.0138116	0.0154923	0.0326106	0.00616531	-0.0147903	0.0676954	-0.00159911	0.0183556	-0.00702118	-0.00256141	nan	nan	nan	nan	0.0503214	-0.0127925	-0.00496629	0.00317881	-0.00351716	-0.00422056	-0.0160728	-0.00925781	-0.0229321	-0.0208823	0.00943032	-0.00966986	-0.0121454	-0.00449231	0.000173295	-0.0029595	0.0019704	-0.00378128	-0.0178815	0.0012872	0.0224391	-0.0023287	-0.00788305	0.634224	0.525145	0.628761	0.409268
dti_joint	0.138557	0.0989231	0.0989231	0.0989095	0.0637183	0.0560887	0.0907054	-0.0694189	-0.0542628	0.12324	nan	0.231641	-0.00373691	0.00987256	0.010836	0.0108358	-0.0196721	-0.0068887	-0.00275022	-0.000784209	-0.00793729	0.00648542	0.0342257	0.00074875	0.0126395	-0.0145247	0.925836	0.803658	1	-0.0020982	-0.0118766	0.0116995	-0.00107468	0.00566236	0.00630324	0.0286964	0.00122126	-0.0239755	-0.0222214	0.00748965	0.0274741	0.0204248	0.036244	0.00762963	-0.0104125	0.0302414	-0.0224684	0.0295882	-0.00673212	-0.0026222	nan	nan	nan	nan	0.0289413	-0.0170789	-0.0111498	0.00414795	-0.0104194	-0.00874896	-0.018663	-0.00485167	-0.029926	-0.0306313	0.0145116	-0.0107856	-0.0173175	0.000179617	-0.000113842	-0.0031668	0.000314872	-0.00559278	-0.0170224	0.00900865	0.0338727	0.00289668	-0.0104434	0.610817	0.473685	0.655014	0.406984
acc_now_delinq	-0.0130889	0.0017271	0.0017271	0.00170044	-0.00242646	0.0065543	0.00338448	0.0108487	0.0128571	-0.00929717	nan	-0.00105407	0.123617	-0.0314438	-0.0428563	-0.0428545	-0.00414494	0.0689108	-0.0054401	0.0224183	-0.00257833	0.00352412	-0.0303393	0.0288889	-0.000441343	0.0355687	-0.0022275	-0.000826569	-0.0020982	1	-0.00761881	0.007005	-0.00485305	-0.00670858	0.00667966	0.00867382	-0.00438624	-0.00527453	-0.00443329	-0.00264773	-0.0250433	-0.00524045	0.00434626	-0.00538684	-0.00690889	0.0139345	0.0184271	-0.0292296	0.0425989	0.203697	nan	nan	nan	nan	0.0262094	-0.00634398	0.0495909	-0.000893305	0.062975	0.0182332	-0.00295717	0.00554845	0.00178831	0.0176981	0.00768579	0.0167498	0.0284013	0.00383753	0.0115189	0.40493	0.795964	0.0583675	-0.00717039	-0.0489672	-0.0262401	-0.0115422	0.00792196	-0.00329687	-0.00300911	-0.00518892	-0.00104258
open_acc_6m	-0.0192934	-0.0153681	-0.0153681	-0.0154125	0.0139953	0.170999	0.00464014	0.0226559	0.0373415	-0.0237732	nan	0.0302801	-0.000239808	-0.0118584	-0.0616149	-0.0616173	0.424702	0.0491603	0.0661928	0.281506	0.0568102	-0.0193387	-0.181813	0.258755	-0.0436611	0.0797902	-0.0156001	-0.0113082	-0.0118766	-0.00761881	1	0.0800516	0.38216	0.29324	0.0465493	0.11244	0.161022	0.616661	0.474521	-0.0835329	-0.0462331	0.153737	0.0913306	0.308557	0.549247	-0.0335654	0.099022	-0.160855	0.0018867	-0.00455355	nan	nan	nan	nan	0.0499448	0.0257039	0.0328249	0.151626	0.035032	0.0597546	0.134118	0.206671	0.1967	0.191733	0.136626	0.277622	0.245263	0.19597	0.280231	-0.00618333	-0.00669984	0.0095132	0.720908	0.00667259	-0.151688	0.0558972	0.0152902	-0.0126559	-0.00728085	-0.0204226	-0.00799011
open_act_il	-0.00926293	0.0304857	0.0304857	0.0304666	0.0343187	0.0333187	0.0273263	-0.0626386	0.0633142	-0.0104031	nan	0.173504	0.0640688	0.0583278	-0.0134152	-0.0134182	0.0306383	0.0527387	-0.0315764	0.51498	-0.028641	-0.0012892	0.0458067	0.373834	0.00142055	0.035763	-0.00278045	0.00611445	0.0116995	0.007005	0.0800516	1	0.270178	0.35514	0.147997	0.556975	0.426248	-0.0165188	-0.0177898	0.0102881	0.371163	0.0892446	0.0855759	0.0774622	0.164241	-0.0494235	-0.0312765	0.045203	-0.00194676	-0.00280131	nan	nan	nan	nan	-0.0309252	-0.00353267	0.0142301	0.0499328	0.0199633	0.0883981	-0.0070089	-0.0021399	-0.0173969	-0.0285025	0.630083	-0.00685534	-0.0186272	0.00133266	0.517462	0.000432986	0.00593036	0.0614521	0.12235	-0.0109671	0.0425085	-0.0286686	-0.0122823	0.00421186	-0.00247352	-0.00348556	-0.000702324
open_il_12m	-0.0218833	0.00200697	0.00200697	0.00198669	0.0419519	0.19403	0.0181085	0.0474697	0.0797827	-0.0312628	nan	0.131945	-0.00885671	-0.00247454	-0.0216399	-0.0216427	0.145311	0.0388924	0.0486684	0.167873	0.0374344	-0.0352578	-0.0838585	0.254223	-0.0390542	0.0460466	-0.013887	-0.00859938	-0.00107468	-0.00485305	0.38216	0.270178	1	0.754878	0.125331	0.297526	0.381355	0.0636213	0.067018	-0.0637622	0.189743	0.259924	0.208412	0.320585	0.440723	0.0238506	0.00233076	-0.079635	-0.0027676	-0.00445873	nan	nan	nan	nan	0.0575597	-0.0132939	0.0187393	0.12935	0.0155649	0.0379043	-0.0396431	-0.0164143	-0.00605558	0.0258083	0.346195	0.0223643	0.0443078	-0.0193469	0.168107	-0.00431037	-0.00401501	-0.00208664	0.561705	0.0264624	-0.0685178	0.0463047	0.00654293	-0.00521119	-0.00469154	-0.0112569	-0.00325605
open_il_24m	0.00589419	0.0315075	0.0315075	0.0314859	0.0627368	0.171914	0.0411935	0.0610861	0.102467	-0.00431884	nan	0.180339	-0.0258594	0.00875091	-0.017437	-0.0174411	0.117973	0.0373714	0.0540555	0.237871	0.0381032	-0.0244266	-0.0664247	0.348491	-0.0274876	0.0300871	-0.0119551	-0.00526274	0.00566236	-0.00670858	0.29324	0.35514	0.754878	1	0.165277	0.365932	0.396888	0.0518012	0.0782004	-0.0536387	0.208603	0.343601	0.2814	0.264266	0.574646	0.0240117	0.00714786	-0.0613072	-0.00237151	-0.00463704	nan	nan	nan	nan	0.0783013	-0.0170853	0.0081386	0.151223	0.00441684	0.0280061	-0.0254716	0.000797204	0.000984645	0.0392207	0.470968	0.0505349	0.0642075	0.00639011	0.239377	-0.00534064	-0.00540845	-0.022563	0.431285	0.0651893	-0.0520546	0.0526413	0.00415751	-0.00377276	-0.00241752	-0.0122527	-0.00416827
mths_since_rcnt_il	-0.00312323	0.0499328	0.0499328	0.0499276	0.0352736	-0.0102362	0.0420945	0.0523735	0.0499242	-0.00258569	nan	0.11519	0.0277418	-0.036872	-0.00255944	-0.00256083	0.0198792	0.0681927	0.0218196	0.108818	0.0166762	0.0215199	0.01289	0.16105	0.0124691	0.049313	-0.00150543	0.00420784	0.00630324	0.00667966	0.0465493	0.147997	0.125331	0.165277	1	0.134028	0.307842	-6.7887e-05	-0.000691562	0.0229712	0.100231	0.0744073	0.0835565	0.0620311	0.0881004	0.0497583	-0.00460383	0.01612	0.00873132	0.0020377	nan	nan	nan	nan	0.0741814	-0.00596338	0.0249249	0.0588964	0.0323102	0.0352813	-0.00245439	0.0172447	0.00293162	0.0289562	0.184509	0.0271208	0.0518723	0.0171077	0.108734	0.00112023	0.00523758	0.0163838	0.0683615	-0.0239216	0.0133864	0.0212014	0.00247218	-0.000141324	0.00206934	-0.00470371	-0.00408784
total_bal_il	0.0045272	0.157087	0.157087	0.157077	0.0769668	0.0376085	0.146332	-0.00350992	0.201533	0.0028623	nan	0.192938	0.0485201	-0.0285505	0.0117294	0.0117265	0.0504319	0.0643094	-0.0264372	0.342034	-0.0223009	0.0925043	0.0328792	0.407291	0.0131395	0.0439697	0.011471	0.0306911	0.0286964	0.00867382	0.11244	0.556975	0.297526	0.365932	0.134028	1	0.345969	-0.000897383	-0.00296172	0.0971311	0.294235	0.145066	0.11999	0.149457	0.190607	0.191673	0.0400949	0.0233544	0.00109849	-0.000601912	nan	nan	nan	nan	0.0811057	0.00422169	0.0201831	0.0801214	0.024244	0.0713369	0.0257781	0.0279362	0.0347752	0.042446	0.579082	0.0427055	0.0503017	0.0300477	0.34343	0.00390574	0.00531998	0.0370412	0.156345	0.00849149	0.0168712	-0.0283581	0.000789849	0.0222411	0.0140907	0.00881707	0.00681597
il_util	-0.0244249	-0.0383439	-0.0383439	-0.0383544	0.042335	0.125128	-0.0330059	-0.019046	0.00228085	-0.0295579	nan	0.165749	0.0141296	0.0601059	-0.0818219	-0.0818255	0.0717362	0.0429723	0.0319805	0.21282	0.0232084	-0.0592588	-0.00230849	0.185937	-0.0197974	0.0624107	-0.0107439	-0.0107355	0.00122126	-0.00438624	0.161022	0.426248	0.381355	0.396888	0.307842	0.345969	1	0.0298274	0.0387439	-0.0700049	0.511279	0.151635	0.0935839	0.149637	0.231345	-0.036966	-0.0735395	0.00819367	0.00013921	-0.00579825	nan	nan	nan	nan	-0.0222128	-0.00788238	0.0086735	0.113513	0.0106431	0.0716097	-0.0312738	-0.00813602	-0.0307332	-0.0396905	0.328646	-0.0140658	-0.0209398	-0.0139502	0.2131	-0.00287632	-0.00583002	0.0332349	0.216616	-0.0211727	0.00629931	0.0326699	-0.00220551	-0.0121975	-0.0122938	-0.0133814	-0.00771851
open_rv_12m	-0.0139552	-0.034697	-0.034697	-0.0347373	-0.0146462	0.150425	-0.00946319	0.00905316	-0.00400232	-0.0136302	nan	-0.000557731	-0.0234147	0.00055375	-0.109483	-0.109485	0.341016	0.0417107	0.093185	0.369619	0.0807145	-0.00142028	-0.197121	0.257423	-0.0463635	0.0929728	-0.0262963	-0.0234255	-0.0239755	-0.00527453	0.616661	-0.0165188	0.0636213	0.0518012	-6.7887e-05	-0.000897383	0.0298274	1	0.773266	-0.0893363	-0.161308	0.0942083	0.00654725	0.311212	0.648032	-0.149873	0.130773	-0.15384	-0.000782398	-0.00272689	nan	nan	nan	nan	-0.0264225	0.0543993	0.0314165	0.158242	0.0336248	0.070966	0.273071	0.369663	0.332636	0.300954	0.0124119	0.455826	0.376212	0.356516	0.367311	-0.00472218	-0.00491244	-0.000297686	0.837077	0.00695222	-0.155791	0.0796004	0.0217304	-0.0186303	-0.0179965	-0.0269882	-0.0106544
open_rv_24m	-0.0177426	-0.017261	-0.017261	-0.0173085	-0.00613441	0.158809	0.00898484	0.00405258	-0.0103785	-0.0139293	nan	0.0239507	-0.0489541	0.024354	-0.133455	-0.133456	0.298656	0.0353053	0.122713	0.473997	0.103701	0.00277935	-0.215097	0.321715	-0.044386	0.0899842	-0.0262304	-0.0238085	-0.0222214	-0.00443329	0.474521	-0.0177898	0.067018	0.0782004	-0.000691562	-0.00296172	0.0387439	0.773266	1	-0.103536	-0.175801	0.13306	0.0131964	0.297171	0.842025	-0.196518	0.147483	-0.152172	0.00408837	-0.00160363	nan	nan	nan	nan	-0.0467358	0.0640103	0.02146	0.199499	0.0255257	0.0694876	0.346665	0.461232	0.412793	0.377464	0.0117532	0.584674	0.476877	0.45121	0.471632	-0.00401581	-0.00452248	-0.0229226	0.654754	0.0397782	-0.153979	0.111202	0.0252402	-0.020129	-0.0217354	-0.0307799	-0.0121039
max_bal_bc	-0.0219051	0.353818	0.353818	0.353833	0.0903663	-0.0424092	0.331862	0.0713191	0.24945	-0.0016616	nan	0.0851506	-0.0515156	-0.190607	0.0460722	0.0460666	-0.0542851	-0.0817066	-0.144157	0.106785	-0.114796	0.546885	0.3258	0.121812	0.0388421	-0.115907	0.000584367	0.0234647	0.00748965	-0.00264773	-0.0835329	0.0102881	-0.0637622	-0.0536387	0.0229712	0.0971311	-0.0700049	-0.0893363	-0.103536	1	0.131186	-0.0786183	-0.0194046	-0.078415	-0.0993511	0.255621	0.177813	0.305404	-0.016073	-0.00398507	nan	nan	nan	nan	0.212922	0.108332	-0.0603317	-0.0491943	-0.062233	-0.0997827	0.217399	0.124489	0.206914	0.182499	0.0141902	0.0895558	0.116729	0.11929	0.106525	-0.00680483	0.00441199	-0.0462697	-0.0968394	0.128772	0.217872	-0.129389	-0.0217796	0.0469201	0.0172944	0.00881723	-0.0134969
all_util	-0.0811412	0.0146875	0.0146875	0.0146675	0.050348	0.25345	0.0367535	-0.0147168	0.0067374	-0.0746282	nan	0.170675	0.0213504	0.0536393	-0.416169	-0.41617	-0.048355	0.053505	-0.0136064	-0.0123935	-0.00883367	0.109251	0.65511	-0.00023709	-0.0430698	0.0576812	0.016174	0.0138116	0.0274741	-0.0250433	-0.0462331	0.371163	0.189743	0.208603	0.100231	0.294235	0.511279	-0.161308	-0.175801	0.131186	1	0.0754531	0.062601	0.0274413	-0.0408526	0.0913201	-0.47187	0.565641	-0.00831581	-0.00685925	nan	nan	nan	nan	-0.0244293	-0.0292938	0.018079	0.0191668	0.0149528	0.0760154	-0.0328463	-0.0213858	-0.2125	-0.220668	0.256207	-0.249764	-0.228374	-0.0026557	-0.0110648	-0.00707463	-0.0217334	0.0324189	-0.0402328	-0.0762987	0.478858	-0.0158144	-0.00264618	0.00676926	-0.00215361	0.016229	-0.00191463
inq_fi	0.0338149	0.00764786	0.00764786	0.00763789	0.027585	0.145232	0.0209666	0.0092254	0.053162	0.0260245	nan	0.055609	0.0229035	0.0292811	-0.0815677	-0.0815686	0.218088	0.073993	0.0762482	0.108395	0.0674735	-0.0468321	-0.0734558	0.16573	-0.0301753	0.069348	0.0142427	0.0154923	0.0204248	-0.00524045	0.153737	0.0892446	0.259924	0.343601	0.0744073	0.145066	0.151635	0.0942083	0.13306	-0.0786183	0.0754531	1	0.0898335	0.563693	0.302884	0.0517894	-0.00437465	-0.0618109	0.0107957	-0.000685353	nan	nan	nan	nan	0.090355	0.00264659	0.041065	0.211763	0.0369582	0.0677987	-0.000722646	0.00851313	0.0132135	0.049748	0.173357	0.0622347	0.0629156	0.021789	0.109912	-0.00292626	-0.0065604	0.00873659	0.228857	-0.017259	-0.0614376	0.0731705	0.0202849	0.00407757	0.0118063	0.00628767	0.0133634
total_cu_tl	0.00451937	0.0695528	0.0695528	0.0695417	0.0538909	0.0204437	0.0597467	0.0834455	0.0419613	0.00432069	nan	0.09578	0.0172532	-0.0403152	-0.00267085	-0.00267086	0.0313672	0.0386346	0.00847277	0.101001	-0.00157323	0.0357439	0.0355184	0.29089	0.0112963	0.00367465	0.0281037	0.0326106	0.036244	0.00434626	0.0913306	0.0855759	0.208412	0.2814	0.0835565	0.11999	0.0935839	0.00654725	0.0131964	-0.0194046	0.062601	0.0898335	1	0.0789875	0.165896	0.0591046	-0.0162122	-0.0169612	0.00400361	0.00055474	nan	nan	nan	nan	0.165511	-0.0855086	0.000451111	0.0373086	0.025179	-0.00219945	-0.0817919	0.0158443	-0.0685984	0.00226729	0.304966	0.0507981	0.112094	0.0266929	0.101881	0.00140141	0.00234959	-0.00263983	0.121665	0.0470462	-0.000464966	0.0172131	-0.014358	0.0245087	0.0345538	0.0207548	0.00824441
inq_last_12m	-0.00235927	0.0164975	0.0164975	0.0164552	0.0303021	0.1967	0.037195	0.000861288	0.064537	-0.00388903	nan	0.0308119	0.0264162	0.0223407	-0.126787	-0.126788	0.484791	0.074587	0.105516	0.184489	0.0904722	-0.0253142	-0.123023	0.213623	-0.0453963	0.0917672	0.00287337	0.00616531	0.00762963	-0.00538684	0.308557	0.0774622	0.320585	0.264266	0.0620311	0.149457	0.149637	0.311212	0.297171	-0.078415	0.0274413	0.563693	0.0789875	1	0.398087	0.0410276	0.0248997	-0.101845	0.00925652	-0.003314	nan	nan	nan	nan	0.0974853	0.0163791	0.0450641	0.283332	0.0430992	0.0777948	0.0551404	0.0958435	0.0992322	0.120394	0.148112	0.159422	0.155754	0.0933912	0.183727	-0.00495738	-0.00562695	0.0160847	0.448994	-0.0192305	-0.103555	0.104239	0.0228439	-0.00750618	0.00157327	-0.00676538	0.00452644
acc_open_past_24mths	-0.00409831	0.019626	0.019626	0.0195828	0.0368651	0.208535	0.0424672	0.038457	0.0610748	-0.00624408	nan	0.101628	-0.0585419	0.021144	-0.10921	-0.109214	0.303038	0.047586	0.123906	0.51123	0.0998176	-0.00365459	-0.209715	0.455349	-0.0452869	0.0852758	-0.0214221	-0.0147903	-0.0104125	-0.00690889	0.549247	0.164241	0.440723	0.574646	0.0881004	0.190607	0.231345	0.648032	0.842025	-0.0993511	-0.0408526	0.302884	0.165896	0.398087	1	-0.0803721	0.133892	-0.15783	0.00283212	-0.00440563	nan	nan	nan	nan	0.0719896	0.0427255	0.0199098	0.256422	0.020815	0.0679818	0.264383	0.368763	0.335204	0.328338	0.2571	0.496488	0.42004	0.363311	0.510226	-0.00651912	-0.00613127	-0.0341718	0.771942	0.0729495	-0.154705	0.114637	0.0208618	-0.0135357	-0.00818275	-0.0270999	-0.0106693
avg_cur_bal	0.027256	0.215998	0.215998	0.216055	0.0757197	-0.0883193	0.184681	0.0955353	0.287459	0.0272625	nan	-0.0724721	0.0317852	-0.118321	0.10444	0.104442	-0.0440917	0.0490007	-0.0736874	-0.124814	-0.0579094	0.281683	0.142375	0.0360446	0.0415878	0.0144104	0.0427614	0.0676954	0.0302414	0.0139345	-0.0335654	-0.0494235	0.0238506	0.0240117	0.0497583	0.191673	-0.036966	-0.149873	-0.196518	0.255621	0.0913201	0.0517894	0.0591046	0.0410276	-0.0803721	1	0.028102	0.0720545	0.00425261	0.0225698	nan	nan	nan	nan	0.462622	-0.0506585	0.0296503	0.0319576	0.0231014	0.013306	-0.113039	-0.149751	-0.118107	-0.0580143	0.0448754	-0.200781	-0.0945567	-0.153349	-0.125071	0.00738516	0.013113	0.00446792	-0.0495513	-0.0435262	0.0709961	-0.0807372	-0.00148414	0.0469711	0.0618051	0.0416053	0.0160454
bc_open_to_buy	0.0610435	0.188939	0.188939	0.188967	0.0209426	-0.244328	0.150411	0.0212737	0.153362	0.0629564	nan	-0.063867	-0.061073	-0.140977	0.507928	0.507929	0.0318196	-0.111267	-0.113859	0.305943	-0.0907413	0.150496	-0.464889	0.244732	0.0553371	-0.125023	-0.0195873	-0.00159911	-0.0224684	0.0184271	0.099022	-0.0312765	0.00233076	0.00714786	-0.00460383	0.0400949	-0.0735395	0.130773	0.147483	0.177813	-0.47187	-0.00437465	-0.0162122	0.0248997	0.133892	0.028102	1	-0.500072	-0.0114597	0.00159527	nan	nan	nan	nan	0.133413	0.0748705	-0.063607	0.00754838	-0.0748285	-0.0818461	0.245744	0.105946	0.484976	0.459524	-0.00451172	0.371504	0.331844	0.122936	0.309204	0.00566647	0.0168384	-0.0442112	0.117169	0.165385	-0.411689	-0.0997203	-0.0197553	0.0149981	0.00844124	-0.0193241	-0.00872533
bc_util	-0.091366	0.0761079	0.0761079	0.0760853	0.0446585	0.210012	0.098197	0.0420823	0.0121136	-0.0728878	nan	0.150757	-0.00470567	-0.0234079	-0.470744	-0.470741	-0.0897593	0.0182409	-0.0168851	-0.0741913	-0.0153993	0.191461	0.841099	-0.0744188	-0.028589	0.0105724	0.019681	0.0183556	0.0295882	-0.0292296	-0.160855	0.045203	-0.079635	-0.0613072	0.01612	0.0233544	0.00819367	-0.15384	-0.152172	0.305404	0.565641	-0.0618109	-0.0169612	-0.101845	-0.15783	0.0720545	-0.500072	1	-0.00983678	-0.00690652	nan	nan	nan	nan	0.0105076	0.197854	-0.000993264	-0.0406667	0.00334719	-0.0165676	0.117683	0.154295	-0.128802	-0.153372	0.0135313	-0.124005	-0.123005	0.161241	-0.0750139	-0.012314	-0.0213142	-0.0120727	-0.171062	-0.0324937	0.845272	-0.018847	-0.00370829	0.0106302	-0.00364648	0.0180859	-0.00833916
chargeoff_within_12_mths	-0.000865381	-0.00195514	-0.00195514	-0.00196807	-0.00225203	0.00647974	-0.000521834	0.00673721	0.00481134	0.00153744	nan	-0.00259618	0.152355	-0.0266618	-0.0528395	-0.052837	0.0102862	0.0702054	-0.011433	0.005978	-0.00895587	-0.00967216	-0.0120491	0.0361676	-0.000363524	0.122622	-0.00785714	-0.00702118	-0.00673212	0.0425989	0.0018867	-0.00194676	-0.0027676	-0.00237151	0.00873132	0.00109849	0.00013921	-0.000782398	0.00408837	-0.016073	-0.00831581	0.0107957	0.00400361	0.00925652	0.00283212	0.00425261	-0.0114597	-0.00983678	1	0.0105278	nan	nan	nan	nan	0.019213	0.000453322	0.0558066	0.00759287	0.0591205	0.114215	-0.00658847	0.0038822	-0.00124331	0.0293433	0.00961118	0.00677495	0.038421	0.00106656	0.00553998	0.0359113	0.000856444	0.227076	-0.00218708	-0.0806482	-0.0101618	-0.0154123	-0.000991514	-0.0034651	-0.00369874	-0.00350607	0.0031394
delinq_amnt	-0.00363466	0.00276253	0.00276253	0.00274976	0.000162738	0.00431558	0.00357904	0.00203488	0.00818791	-0.00383244	nan	-0.00724449	0.035054	-0.00867312	-0.0151574	-0.0151572	-0.00224899	0.0207741	0.000426055	0.00331224	0.00281559	0.00223265	-0.00763357	0.0043422	-0.00318297	0.0333099	-0.0028434	-0.00256141	-0.0026222	0.203697	-0.00455355	-0.00280131	-0.00445873	-0.00463704	0.0020377	-0.000601912	-0.00579825	-0.00272689	-0.00160363	-0.00398507	-0.00685925	-0.000685353	0.00055474	-0.003314	-0.00440563	0.0225698	0.00159527	-0.00690652	0.0105278	1	nan	nan	nan	nan	0.0150131	-0.00164215	0.00571492	-0.0035332	0.00885733	0.0220937	-0.001736	0.00036987	-0.000381842	0.00136466	-0.00188584	0.00155509	0.00386748	-0.000938581	-0.00123076	0.367772	0.0381949	0.0505135	-0.00524767	-0.0171228	-0.00763145	-0.0029183	0.0054966	-0.00196265	-0.00178863	-0.00237695	-0.00176629
mo_sin_old_il_acct	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
mo_sin_old_rev_tl_op	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
mo_sin_rcnt_rev_tl_op	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
mo_sin_rcnt_tl	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
mort_acc	-0.0277226	0.212504	0.212504	0.212543	0.0901313	-0.111063	0.175254	0.158505	0.198136	-0.0154189	nan	-0.0128473	0.0571907	-0.27796	0.104666	0.104667	0.0126668	0.107099	-0.0264072	0.128376	-0.0255759	0.216294	0.0337002	0.347698	0.0511063	0.066741	0.0332519	0.0503214	0.0289413	0.0262094	0.0499448	-0.0309252	0.0575597	0.0783013	0.0741814	0.0811057	-0.0222128	-0.0264225	-0.0467358	0.212922	-0.0244293	0.090355	0.165511	0.0974853	0.0719896	0.462622	0.133413	0.0105076	0.019213	0.0150131	nan	nan	nan	nan	1	0.000482828	0.0538963	0.0706693	0.0616658	0.0442576	0.0428493	0.0609731	0.0794778	0.180937	0.0846755	0.0840111	0.211166	0.0582078	0.126177	0.0097598	0.0217753	0.0132967	0.0666973	-0.0144858	0.0126638	-0.0174074	-0.0178562	0.0361079	0.0868426	0.0186783	0.00742479
mths_since_recent_bc	-0.0151451	0.0462264	0.0462264	0.0462363	0.0152259	-0.0289126	0.0421676	0.00463817	0.0176033	-0.00796961	nan	0.00830909	-0.0158934	-0.00855518	0.00784537	0.00784578	0.0173994	-0.0147326	0.00549048	0.0907996	0.005651	0.0414679	0.0104042	0.0360243	0.0128764	-0.00798391	-0.0190638	-0.0127925	-0.0170789	-0.00634398	0.0257039	-0.00353267	-0.0132939	-0.0170853	-0.00596338	0.00422169	-0.00788238	0.0543993	0.0640103	0.108332	-0.0292938	0.00264659	-0.0855086	0.0163791	0.0427255	-0.0506585	0.0748705	0.197854	0.000453322	-0.00164215	nan	nan	nan	nan	0.000482828	1	-0.0010856	0.016384	-0.0138369	-0.0130127	0.165875	0.103923	0.163902	0.116076	-0.0184111	0.110806	0.069867	0.105189	0.0908174	-0.0011123	-0.0060109	-0.0106523	0.0373853	0.0315371	0.116542	0.00425672	0.00565532	-0.00607725	-0.0114841	-0.0225751	-0.014197
mths_since_recent_bc_dlq	-0.0203849	-0.0068254	-0.0068254	-0.00685981	-0.00936181	0.0239265	-0.000387367	0.0261163	0.0295602	-0.0223056	nan	-0.0249932	0.317727	-0.120467	-0.202268	-0.202266	0.0349001	0.550657	-0.05204	0.0272822	-0.0322887	-0.0326004	-0.00121411	0.126063	-0.00611035	0.383217	-0.00984521	-0.00496629	-0.0111498	0.0495909	0.0328249	0.0142301	0.0187393	0.0081386	0.0249249	0.0201831	0.0086735	0.0314165	0.02146	-0.0603317	0.018079	0.041065	0.000451111	0.0450641	0.0199098	0.0296503	-0.063607	-0.000993264	0.0558066	0.00571492	nan	nan	nan	nan	0.0538963	-0.0010856	1	0.0289446	0.777202	0.319289	-0.01046	0.00367685	-0.0108362	0.14231	0.038387	0.0165989	0.136835	0.00477741	0.0263332	0.0124995	0.045094	0.114447	0.0341549	-0.520245	-0.00849793	-0.0823344	0.0113915	-0.0135996	-0.00428139	-0.00994155	0.016833
mths_since_recent_inq	0.00170865	0.0138877	0.0138877	0.0138697	0.0248917	0.095931	0.0207774	0.00435118	0.0316428	0.000616289	nan	0.0291981	0.0128847	0.019383	-0.094217	-0.0942215	0.209879	0.0537591	0.0695888	0.128645	0.0573158	-0.00693511	-0.063428	0.132395	-0.0138002	0.0621868	0.00122771	0.00317881	0.00414795	-0.000893305	0.151626	0.0499328	0.12935	0.151223	0.0588964	0.0801214	0.113513	0.158242	0.199499	-0.0491943	0.0191668	0.211763	0.0373086	0.283332	0.256422	0.0319576	0.00754838	-0.0406667	0.00759287	-0.0035332	nan	nan	nan	nan	0.0706693	0.016384	0.0289446	1	0.0319992	0.0444397	0.0569204	0.090525	0.0780788	0.079089	0.0785013	0.112131	0.107386	0.0857591	0.127483	-0.00225567	-0.00016021	0.00881176	0.211114	-0.017555	-0.0454136	0.0656321	0.0119967	-0.00719881	0.00214206	-0.00855814	-0.000433285
mths_since_recent_revol_delinq	-0.0187132	-0.00389924	-0.00389924	-0.00393152	-0.0117617	0.0266176	0.00415115	0.030035	0.0280972	-0.0197745	nan	-0.0196061	0.352637	-0.132552	-0.236348	-0.236346	0.0338875	0.707576	-0.0663142	0.0595541	-0.0431191	-0.0274949	-0.00476487	0.153044	-0.00568527	0.40456	-0.00934307	-0.00351716	-0.0104194	0.062975	0.035032	0.0199633	0.0155649	0.00441684	0.0323102	0.024244	0.0106431	0.0336248	0.0255257	-0.062233	0.0149528	0.0369582	0.025179	0.0430992	0.020815	0.0231014	-0.0748285	0.00334719	0.0591205	0.00885733	nan	nan	nan	nan	0.0616658	-0.0138369	0.777202	0.0319992	1	0.302119	-0.0252805	0.0250356	-0.0238033	0.108153	0.0488154	0.050745	0.165765	0.0252108	0.0581276	0.0164159	0.0572876	0.116126	0.0339703	-0.550831	-0.00396584	-0.0982129	0.00937857	-0.0130986	-0.0033705	-0.00786351	0.0195779
num_accts_ever_120_pd	-0.00189675	-0.043254	-0.043254	-0.0432889	-0.0171136	0.0362125	-0.0346932	0.00493574	0.00959516	-0.00689764	nan	-0.0274789	0.216559	-0.0854754	-0.194331	-0.194328	0.0462168	0.362446	-0.0108138	0.0314553	0.00321878	-0.070373	-0.0197588	0.145121	-0.0122598	0.584653	-0.00791016	-0.00422056	-0.00874896	0.0182332	0.0597546	0.0883981	0.0379043	0.0280061	0.0352813	0.0713369	0.0716097	0.070966	0.0694876	-0.0997827	0.0760154	0.0677987	-0.00219945	0.0777948	0.0679818	0.013306	-0.0818461	-0.0165676	0.114215	0.0220937	nan	nan	nan	nan	0.0442576	-0.0130127	0.319289	0.0444397	0.302119	1	-0.0440399	-0.0241404	-0.0627028	0.0522256	0.145643	-0.0189137	0.0642757	-0.0157037	0.0322784	0.0322363	-0.00596655	0.317129	0.0752611	-0.589166	-0.0238768	-0.0468898	0.00973285	-0.0138636	-0.00630334	-0.00817286	0.0199888
num_actv_bc_tl	-0.0346523	0.199607	0.199607	0.199572	0.0527023	0.0534045	0.20317	0.0758387	0.106956	-0.0152928	nan	0.126229	-0.0328287	-0.116417	-0.120806	-0.120807	0.101559	-0.0361648	-0.0466023	0.551326	-0.029659	0.309452	0.114271	0.304611	0.00199673	-0.0278289	-0.0311423	-0.0160728	-0.018663	-0.00295717	0.134118	-0.0070089	-0.0396431	-0.0254716	-0.00245439	0.0257781	-0.0312738	0.273071	0.346665	0.217399	-0.0328463	-0.000722646	-0.0817919	0.0551404	0.264383	-0.113039	0.245744	0.117683	-0.00658847	-0.001736	nan	nan	nan	nan	0.0428493	0.165875	-0.01046	0.0569204	-0.0252805	-0.0440399	1	0.820425	0.82043	0.595364	-0.0194805	0.650644	0.45893	0.808971	0.548949	-0.00628703	0.000230749	-0.029503	0.197673	0.109193	0.0581039	-0.056417	0.0133264	0.00282292	-0.016675	-0.0271403	-0.0203711
num_actv_rev_tl	-0.0549845	0.161251	0.161251	0.161207	0.0559652	0.0947076	0.168627	0.111782	0.0792329	-0.032089	nan	0.181384	0.00268074	-0.15247	-0.19519	-0.19519	0.156783	0.0130495	0.00624367	0.661589	0.00564454	0.309683	0.122157	0.404457	-0.00581582	0.0208443	-0.0208105	-0.00925781	-0.00485167	0.00554845	0.206671	-0.0021399	-0.0164143	0.000797204	0.0172447	0.0279362	-0.00813602	0.369663	0.461232	0.124489	-0.0213858	0.00851313	0.0158443	0.0958435	0.368763	-0.149751	0.105946	0.154295	0.0038822	0.00036987	nan	nan	nan	nan	0.0609731	0.103923	0.00367685	0.090525	0.0250356	-0.0241404	0.820425	1	0.666762	0.489308	0.014075	0.776673	0.574335	0.975518	0.656357	-0.00478624	0.00764551	-0.0145671	0.286416	0.084511	0.111197	-0.00444145	0.00899694	0.0039281	-0.0146125	-0.0214485	-0.0174936
num_bc_sats	-0.0103156	0.220138	0.220138	0.220116	0.0602925	-0.0243449	0.209793	0.0688841	0.123213	0.00675165	nan	0.0754692	-0.026932	-0.13836	0.0642446	0.0642422	0.15623	-0.0490637	-0.0602441	0.6326	-0.044667	0.279112	-0.11625	0.412615	0.0205778	-0.038223	-0.0395794	-0.0229321	-0.029926	0.00178831	0.1967	-0.0173969	-0.00605558	0.000984645	0.00293162	0.0347752	-0.0307332	0.332636	0.412793	0.206914	-0.2125	0.0132135	-0.0685984	0.0992322	0.335204	-0.118107	0.484976	-0.128802	-0.00124331	-0.000381842	nan	nan	nan	nan	0.0794778	0.163902	-0.0108362	0.0780788	-0.0238033	-0.0627028	0.82043	0.666762	1	0.754651	-0.00595212	0.749365	0.598343	0.637555	0.628036	-0.00275183	0.00194617	-0.0238053	0.267746	0.13986	-0.133136	-0.0644459	0.00332998	0.000639426	-0.0159482	-0.0355712	-0.022067
num_bc_tl	-0.0429805	0.199425	0.199425	0.199397	0.0500086	-0.0745048	0.184321	0.0899515	0.128081	-0.0233043	nan	0.0552756	0.0308758	-0.263238	0.0737801	0.0737779	0.141211	0.0499351	-0.0142455	0.5457	-0.0180016	0.232235	-0.150926	0.621164	0.0304233	0.0409552	-0.0383825	-0.0208823	-0.0306313	0.0176981	0.191733	-0.0285025	0.0258083	0.0392207	0.0289562	0.042446	-0.0396905	0.300954	0.377464	0.182499	-0.220668	0.049748	0.00226729	0.120394	0.328338	-0.0580143	0.459524	-0.153372	0.0293433	0.00136466	nan	nan	nan	nan	0.180937	0.116076	0.14231	0.079089	0.108153	0.0522256	0.595364	0.489308	0.754651	1	0.0427789	0.651757	0.836398	0.487024	0.54168	0.00525794	0.0146068	0.0110842	0.260238	0.0655869	-0.145449	-0.00593851	-0.010921	-0.00109308	-0.00938568	-0.0375617	-0.0194202
num_il_tl	-0.00498769	0.0729088	0.0729088	0.0728883	0.0653109	0.0154559	0.0598414	0.00275573	0.0929922	-0.00598991	nan	0.157749	0.082474	-0.0181363	-0.0171835	-0.0171868	0.0668369	0.120277	-0.0135545	0.38191	-0.0178215	0.0125949	0.0158534	0.69256	0.0147074	0.0925125	-0.00106958	0.00943032	0.0145116	0.00768579	0.136626	0.630083	0.346195	0.470968	0.184509	0.579082	0.328646	0.0124119	0.0117532	0.0141902	0.256207	0.173357	0.304966	0.148112	0.2571	0.0448754	-0.00451172	0.0135313	0.00961118	-0.00188584	nan	nan	nan	nan	0.0846755	-0.0184111	0.038387	0.0785013	0.0488154	0.145643	-0.0194805	0.014075	-0.00595212	0.0427789	1	0.0458202	0.0864782	0.0192931	0.383402	0.00119655	0.00493631	0.0727621	0.194377	-0.00339816	0.0173363	-0.00935613	-0.0159769	0.00496934	0.00691429	-0.00402448	0.000384541
num_op_rev_tl	-0.0274384	0.160412	0.160412	0.160384	0.0507575	0.00648551	0.156451	0.0982971	0.0782448	-0.00992501	nan	0.125151	0.0110338	-0.169198	0.0134573	0.0134556	0.175681	0.0219983	0.0195522	0.840337	0.0107301	0.226513	-0.201499	0.577508	0.0106175	-0.00827835	-0.0229003	-0.00966986	-0.0107856	0.0167498	0.277622	-0.00685534	0.0223643	0.0505349	0.0271208	0.0427055	-0.0140658	0.455826	0.584674	0.0895558	-0.249764	0.0622347	0.0507981	0.159422	0.496488	-0.200781	0.371504	-0.124005	0.00677495	0.00155509	nan	nan	nan	nan	0.0840111	0.110806	0.0165989	0.112131	0.050745	-0.0189137	0.650644	0.776673	0.749365	0.651757	0.0458202	1	0.794653	0.782106	0.839103	0.00199449	0.0154537	-0.0225507	0.379347	0.130167	-0.123017	0.0181009	0.000311432	0.00652393	-0.00947076	-0.0276161	-0.0144157
num_rev_accts	-0.0466677	0.163052	0.163052	0.163024	0.0501363	-0.0537557	0.149827	0.11622	0.0984909	-0.0268819	nan	0.097101	0.0751926	-0.307887	0.0295203	0.0295188	0.172687	0.103071	0.035464	0.671288	0.0135757	0.215664	-0.181344	0.761235	0.0249612	0.0716812	-0.0268364	-0.0121454	-0.0173175	0.0284013	0.245263	-0.0186272	0.0443078	0.0642075	0.0518723	0.0503017	-0.0209398	0.376212	0.476877	0.116729	-0.228374	0.0629156	0.112094	0.155754	0.42004	-0.0945567	0.331844	-0.123005	0.038421	0.00386748	nan	nan	nan	nan	0.211166	0.069867	0.136835	0.107386	0.165765	0.0642757	0.45893	0.574335	0.598343	0.836398	0.0864782	0.794653	1	0.570161	0.665393	0.00804668	0.0240981	0.0238512	0.329466	0.0533712	-0.112284	0.0486757	-0.0196402	0.00282971	-0.00336047	-0.0300403	-0.0132694
num_rev_tl_bal_gt_0	-0.0602487	0.157129	0.157129	0.157086	0.0528589	0.0955494	0.165577	0.112049	0.077653	-0.0363046	nan	0.184929	-0.00182585	-0.149252	-0.195455	-0.195455	0.129672	0.0165125	0.00290667	0.663984	0.00396216	0.308051	0.130536	0.405051	-0.00742326	0.00823319	-0.0160854	-0.00449231	0.000179617	0.00383753	0.19597	0.00133266	-0.0193469	0.00639011	0.0171077	0.0300477	-0.0139502	0.356516	0.45121	0.11929	-0.0026557	0.021789	0.0266929	0.0933912	0.363311	-0.153349	0.122936	0.161241	0.00106656	-0.000938581	nan	nan	nan	nan	0.0582078	0.105189	0.00477741	0.0857591	0.0252108	-0.0157037	0.808971	0.975518	0.637555	0.487024	0.0192931	0.782106	0.570161	1	0.662889	-0.00403072	0.00647178	-0.0221875	0.273814	0.0898938	0.117669	-0.00757742	0.010145	0.00735437	-0.0124839	-0.0186286	-0.0158036
num_sats	-0.0309258	0.176213	0.176213	0.176186	0.0707827	0.0108543	0.16713	0.0659711	0.125925	-0.0163005	nan	0.195886	0.049985	-0.133176	0.0240883	0.0240852	0.163325	0.0554331	-0.0173759	0.998662	-0.019072	0.215405	-0.135513	0.708356	0.0153477	0.0135839	-0.0177416	0.000173295	-0.000113842	0.0115189	0.280231	0.517462	0.168107	0.239377	0.108734	0.34343	0.2131	0.367311	0.471632	0.106525	-0.0110648	0.109912	0.101881	0.183727	0.510226	-0.125071	0.309204	-0.0750139	0.00553998	-0.00123076	nan	nan	nan	nan	0.126177	0.0908174	0.0263332	0.127483	0.0581276	0.0322784	0.548949	0.656357	0.628036	0.54168	0.383402	0.839103	0.665393	0.662889	1	0.000914236	0.00987463	0.0141552	0.389719	0.10137	-0.0758054	-0.0192534	-0.00727364	0.0113808	-0.00345781	-0.0229484	-0.0119976
num_tl_120dpd_2m	-0.00595114	0.0013363	0.0013363	0.00131546	-0.00132722	0.00235276	0.00192625	0.00146513	0.00766741	-0.00344101	nan	-0.00494301	0.0472085	-0.0106247	-0.0180953	-0.018095	-0.00281417	0.0287721	0.00103694	0.00466632	0.00475399	-0.00285345	-0.0146131	0.00882538	0.000884112	0.0468141	-0.00323222	-0.0029595	-0.0031668	0.40493	-0.00618333	0.000432986	-0.00431037	-0.00534064	0.00112023	0.00390574	-0.00287632	-0.00472218	-0.00401581	-0.00680483	-0.00707463	-0.00292626	0.00140141	-0.00495738	-0.00651912	0.00738516	0.00566647	-0.012314	0.0359113	0.367772	nan	nan	nan	nan	0.0097598	-0.0011123	0.0124995	-0.00225567	0.0164159	0.0322363	-0.00628703	-0.00478624	-0.00275183	0.00525794	0.00119655	0.00199449	0.00804668	-0.00403072	0.000914236	1	0.00108827	0.0728438	-0.00668362	-0.0202917	-0.0136826	-0.00218899	0.00807619	-0.00224183	-0.00236635	-0.00320265	-0.000394056
num_tl_30dpd	-0.0102585	0.00312459	0.00312459	0.00310217	-0.00163741	0.00567208	0.00431939	0.0105402	0.0105974	-0.00769706	nan	0.001442	0.103201	-0.0273597	-0.0321727	-0.0321722	-0.00666443	0.0562023	-0.00998001	0.0196159	-0.00658831	0.0069155	-0.0215178	0.0229275	-0.000503387	-0.00173777	0.000418642	0.0019704	0.000314872	0.795964	-0.00669984	0.00593036	-0.00401501	-0.00540845	0.00523758	0.00531998	-0.00583002	-0.00491244	-0.00452248	0.00441199	-0.0217334	-0.0065604	0.00234959	-0.00562695	-0.00613127	0.013113	0.0168384	-0.0213142	0.000856444	0.0381949	nan	nan	nan	nan	0.0217753	-0.0060109	0.045094	-0.00016021	0.0572876	-0.00596655	0.000230749	0.00764551	0.00194617	0.0146068	0.00493631	0.0154537	0.0240981	0.00647178	0.00987463	0.00108827	1	0.0047266	-0.0061361	-0.0376677	-0.0183645	-0.0144378	0.00583894	-0.00191861	-0.0017072	-0.00297742	-5.5049e-05
num_tl_90g_dpd_24m	-0.00561178	-0.0185283	-0.0185283	-0.018561	-0.0113272	0.0198795	-0.0139407	-0.00294973	0.00193351	-0.00695552	nan	-0.0118116	0.665355	-0.0329759	-0.101024	-0.101022	0.0277039	0.152675	-0.0169895	0.0159108	-0.0117064	-0.028822	-0.00928085	0.0656149	-0.0117098	0.271122	-0.00540271	-0.00378128	-0.00559278	0.0583675	0.0095132	0.0614521	-0.00208664	-0.022563	0.0163838	0.0370412	0.0332349	-0.000297686	-0.0229226	-0.0462697	0.0324189	0.00873659	-0.00263983	0.0160847	-0.0341718	0.00446792	-0.0442112	-0.0120727	0.227076	0.0505135	nan	nan	nan	nan	0.0132967	-0.0106523	0.114447	0.00881176	0.116126	0.317129	-0.029503	-0.0145671	-0.0238053	0.0110842	0.0727621	-0.0225507	0.0238512	-0.0221875	0.0141552	0.0728438	0.0047266	1	-0.00531493	-0.266931	-0.0138684	-0.0247753	0.00218613	-0.00612248	-0.00436704	-0.00390932	0.00860913
num_tl_op_past_12m	-0.0179269	-0.012524	-0.012524	-0.0125626	0.0181777	0.214301	0.0132499	0.0352015	0.0520409	-0.0211688	nan	0.0566104	-0.0285821	-0.00379716	-0.0953209	-0.0953241	0.353596	0.0539345	0.0987125	0.391381	0.0823809	-0.0157294	-0.206963	0.354305	-0.053438	0.097447	-0.0241895	-0.0178815	-0.0170224	-0.00717039	0.720908	0.12235	0.561705	0.431285	0.0683615	0.156345	0.216616	0.837077	0.654754	-0.0968394	-0.0402328	0.228857	0.121665	0.448994	0.771942	-0.0495513	0.117169	-0.171062	-0.00218708	-0.00524767	nan	nan	nan	nan	0.0666973	0.0373853	0.0341549	0.211114	0.0339703	0.0752611	0.197673	0.286416	0.267746	0.260238	0.194377	0.379347	0.329466	0.273814	0.389719	-0.00668362	-0.0061361	-0.00531493	1	0.0250933	-0.16699	0.0873331	0.0197826	-0.0148852	-0.00887584	-0.0251435	-0.00906105
pct_tl_nvr_dlq	-0.00156829	0.0702641	0.0702641	0.0703008	0.0383303	-0.0472754	0.0553822	-0.0166497	0.000633011	0.00721334	nan	0.0640695	-0.440008	0.0884751	0.296871	0.296867	-0.022165	-0.608835	0.0368363	0.100041	0.0121686	0.0965034	-0.0403029	0.0289515	0.0182647	-0.559342	0.00370765	0.0012872	0.00900865	-0.0489672	0.00667259	-0.0109671	0.0264624	0.0651893	-0.0239216	0.00849149	-0.0211727	0.00695222	0.0397782	0.128772	-0.0762987	-0.017259	0.0470462	-0.0192305	0.0729495	-0.0435262	0.165385	-0.0324937	-0.0806482	-0.0171228	nan	nan	nan	nan	-0.0144858	0.0315371	-0.520245	-0.017555	-0.550831	-0.589166	0.109193	0.084511	0.13986	0.0655869	-0.00339816	0.130167	0.0533712	0.0898938	0.10137	-0.0202917	-0.0376677	-0.266931	0.0250933	1	-0.0186247	0.0854826	-0.0284664	0.0161255	0.00325997	-0.000611669	-0.0305037
percent_bc_gt_75	-0.0760892	0.04447	0.04447	0.0444573	0.0400646	0.205924	0.0651471	0.0374231	0.000613847	-0.0597402	nan	0.133691	-0.00673088	-0.0353742	-0.399495	-0.399491	-0.0854127	0.0043547	-0.0300964	-0.0750634	-0.0295045	0.157006	0.723753	-0.0646244	-0.0279433	-0.00136059	0.0252186	0.0224391	0.0338727	-0.0262401	-0.151688	0.0425085	-0.0685178	-0.0520546	0.0133864	0.0168712	0.00629931	-0.155791	-0.153979	0.217872	0.478858	-0.0614376	-0.000464966	-0.103555	-0.154705	0.0709961	-0.411689	0.845272	-0.0101618	-0.00763145	nan	nan	nan	nan	0.0126638	0.116542	-0.00849793	-0.0454136	-0.00396584	-0.0238768	0.0581039	0.111197	-0.133136	-0.145449	0.0173363	-0.123017	-0.112284	0.117669	-0.0758054	-0.0136826	-0.0183645	-0.0138684	-0.16699	-0.0186247	1	-0.0271813	-0.0128984	0.0131655	0.000574869	0.0229265	-0.00515201
pub_rec_bankruptcies	0.00648718	-0.0722752	-0.0722752	-0.0723112	-0.0025567	0.0569179	-0.0633205	0.00503536	-0.0367303	0.0029982	nan	-0.0127161	-0.064268	-0.0560401	-0.206976	-0.206973	0.0855617	-0.0888289	0.776127	-0.0134098	0.654163	-0.107666	-0.0690148	0.0234767	-0.0149067	-0.0395159	0.00528841	-0.0023287	0.00289668	-0.0115422	0.0558972	-0.0286686	0.0463047	0.0526413	0.0212014	-0.0283581	0.0326699	0.0796004	0.111202	-0.129389	-0.0158144	0.0731705	0.0172131	0.104239	0.114637	-0.0807372	-0.0997203	-0.018847	-0.0154123	-0.0029183	nan	nan	nan	nan	-0.0174074	0.00425672	-0.0823344	0.0656321	-0.0982129	-0.0468898	-0.056417	-0.00444145	-0.0644459	-0.00593851	-0.00935613	0.0181009	0.0486757	-0.00757742	-0.0192534	-0.00218899	-0.0144378	-0.0247753	0.0873331	0.0854826	-0.0271813	1	0.0347124	-0.00917239	-3.80428e-05	-0.00107613	-0.00949286
tax_liens	-0.0176822	0.0132666	0.0132666	0.0132395	-0.00782063	0.0151899	0.018998	0.00531877	0.0350064	-0.0132262	nan	-0.0257022	0.00593368	-0.0281559	-0.0617305	-0.0617298	0.0181919	0.0265877	0.299501	-0.0073593	0.686977	-0.010275	-0.00643908	-0.0252072	-0.00594051	0.019823	-0.00976738	-0.00788305	-0.0104434	0.00792196	0.0152902	-0.0122823	0.00654293	0.00415751	0.00247218	0.000789849	-0.00220551	0.0217304	0.0252402	-0.0217796	-0.00264618	0.0202849	-0.014358	0.0228439	0.0208618	-0.00148414	-0.0197553	-0.00370829	-0.000991514	0.0054966	nan	nan	nan	nan	-0.0178562	0.00565532	0.0113915	0.0119967	0.00937857	0.00973285	0.0133264	0.00899694	0.00332998	-0.010921	-0.0159769	0.000311432	-0.0196402	0.010145	-0.00727364	0.00807619	0.00583894	0.00218613	0.0197826	-0.0284664	-0.0128984	0.0347124	1	-0.00935712	-0.0083721	-0.00923122	-0.00434655
revol_bal_joint	0.173537	0.0917166	0.0917166	0.0917409	0.0546446	0.0305633	0.0794495	-0.0640999	-0.0211773	0.153795	nan	0.157884	-0.00670418	-0.00784146	0.0357951	0.0357942	-0.0193547	-0.0151579	-0.0158114	0.0107744	-0.0161148	0.0350645	0.0198315	0.0101585	0.0176648	-0.020143	0.572076	0.634224	0.610817	-0.00329687	-0.0126559	0.00421186	-0.00521119	-0.00377276	-0.000141324	0.0222411	-0.0121975	-0.0186303	-0.020129	0.0469201	0.00676926	0.00407757	0.0245087	-0.00750618	-0.0135357	0.0469711	0.0149981	0.0106302	-0.0034651	-0.00196265	nan	nan	nan	nan	0.0361079	-0.00607725	-0.0135996	-0.00719881	-0.0130986	-0.0138636	0.00282292	0.0039281	0.000639426	-0.00109308	0.00496934	0.00652393	0.00282971	0.00735437	0.0113808	-0.00224183	-0.00191861	-0.00612248	-0.0148852	0.0161255	0.0131655	-0.00917239	-0.00935712	1	0.629213	0.740915	0.375035
sec_app_mort_acc	0.150042	0.0654644	0.0654644	0.0654809	0.0462316	0.00964099	0.0522894	-0.0570702	-0.0262688	0.135667	nan	0.12818	-0.00149503	-0.0099418	0.0349613	0.0349624	-0.0154953	-0.00237543	-0.00506234	-0.0037312	-0.0083866	0.00766717	0.00492423	0.0147019	0.0160926	-0.00844678	0.504644	0.525145	0.473685	-0.00300911	-0.00728085	-0.00247352	-0.00469154	-0.00241752	0.00206934	0.0140907	-0.0122938	-0.0179965	-0.0217354	0.0172944	-0.00215361	0.0118063	0.0345538	0.00157327	-0.00818275	0.0618051	0.00844124	-0.00364648	-0.00369874	-0.00178863	nan	nan	nan	nan	0.0868426	-0.0114841	-0.00428139	0.00214206	-0.0033705	-0.00630334	-0.016675	-0.0146125	-0.0159482	-0.00938568	0.00691429	-0.00947076	-0.00336047	-0.0124839	-0.00345781	-0.00236635	-0.0017072	-0.00436704	-0.00887584	0.00325997	0.000574869	-3.80428e-05	-0.0083721	0.629213	1	0.598185	0.35519
sec_app_revol_util	0.201232	0.0651616	0.0651616	0.065188	0.0512744	0.0466987	0.0577813	-0.0825761	-0.0391232	0.181264	nan	0.156272	-0.00247188	0.0119592	0.0193274	0.0193286	-0.021927	-0.00673003	-0.00774973	-0.0234721	-0.0103497	0.00317158	0.0295645	-0.020617	0.0132245	-0.0127033	0.674253	0.628761	0.655014	-0.00518892	-0.0204226	-0.00348556	-0.0112569	-0.0122527	-0.00470371	0.00881707	-0.0133814	-0.0269882	-0.0307799	0.00881723	0.016229	0.00628767	0.0207548	-0.00676538	-0.0270999	0.0416053	-0.0193241	0.0180859	-0.00350607	-0.00237695	nan	nan	nan	nan	0.0186783	-0.0225751	-0.00994155	-0.00855814	-0.00786351	-0.00817286	-0.0271403	-0.0214485	-0.0355712	-0.0375617	-0.00402448	-0.0276161	-0.0300403	-0.0186286	-0.0229484	-0.00320265	-0.00297742	-0.00390932	-0.0251435	-0.000611669	0.0229265	-0.00107613	-0.00923122	0.740915	0.598185	1	0.543998
sec_app_mths_since_last_major_derog	0.134191	0.025622	0.025622	0.0256408	0.0269252	0.0322549	0.0232078	-0.0539475	-0.0272948	0.120069	nan	0.0887798	0.0117696	0.0037996	0.00648239	0.00648387	-0.00925314	0.0257454	-0.0118531	-0.0122987	-0.0104793	-0.0122944	-0.00544781	-0.0080504	0.00580819	0.0311284	0.446625	0.409268	0.406984	-0.00104258	-0.00799011	-0.000702324	-0.00325605	-0.00416827	-0.00408784	0.00681597	-0.00771851	-0.0106544	-0.0121039	-0.0134969	-0.00191463	0.0133634	0.00824441	0.00452644	-0.0106693	0.0160454	-0.00872533	-0.00833916	0.0031394	-0.00176629	nan	nan	nan	nan	0.00742479	-0.014197	0.016833	-0.000433285	0.0195779	0.0199888	-0.0203711	-0.0174936	-0.022067	-0.0194202	0.000384541	-0.0144157	-0.0132694	-0.0158036	-0.0119976	-0.000394056	-5.5049e-05	0.00860913	-0.00906105	-0.0305037	-0.00515201	-0.00949286	-0.00434655	0.375035	0.35519	0.543998	1

	id	loan_amnt	term	int_rate	installment	grade	sub_grade	emp_length	annual_inc	issue_d	dti	delinq_2yrs	earliest_cr_line	fico_range_low	fico_range_high	inq_last_6mths	mths_since_last_delinq	mths_since_last_record	open_acc	pub_rec	revol_bal	revol_util	total_acc	initial_list_status	mths_since_last_major_derog	annual_inc_joint	dti_joint	acc_now_delinq	open_acc_6m	open_act_il	open_il_12m	open_il_24m	mths_since_rcnt_il	total_bal_il	il_util	open_rv_12m	open_rv_24m	max_bal_bc	all_util	inq_fi	total_cu_tl	inq_last_12m	acc_open_past_24mths	avg_cur_bal	bc_open_to_buy	bc_util	chargeoff_within_12_mths	delinq_amnt	mort_acc	mths_since_recent_bc	mths_since_recent_bc_dlq	mths_since_recent_inq	mths_since_recent_revol_delinq	num_accts_ever_120_pd	num_actv_bc_tl	num_bc_sats	num_bc_tl	num_il_tl	num_op_rev_tl	num_rev_accts	num_rev_tl_bal_gt_0	num_tl_120dpd_2m	num_tl_30dpd	num_tl_90g_dpd_24m	num_tl_op_past_12m	pct_tl_nvr_dlq	percent_bc_gt_75	pub_rec_bankruptcies	tax_liens	revol_bal_joint	sec_app_mort_acc	sec_app_revol_util	sec_app_mths_since_last_major_derog	home_MORTGAGE	home_NONE	home_OWN	home_RENT	verify_Source Verified	verify_Verified	purp_credit_card	purp_debt_consolidation	purp_home_improvement	purp_house	purp_major_purchase	purp_medical	purp_moving	purp_other	purp_renewable_energy	purp_small_business	purp_vacation	purp_wedding	state_AL	state_AR	state_AZ	state_CA	state_CO	state_CT	state_DC	state_DE	state_FL	state_GA	state_HI	state_ID	state_IL	state_IN	state_KS	state_KY	state_LA	state_MA	state_MD	state_ME	state_MI	state_MN	state_MO	state_MS	state_MT	state_NC	state_ND	state_NE	state_NH	state_NJ	state_NM	state_NV	state_NY	state_OH	state_OK	state_OR	state_PA	state_RI	state_SC	state_SD	state_TN	state_TX	state_UT	state_VA	state_VT	state_WA	state_WI	state_WV	state_WY
count	1.600000e+04	1.600000e+04	16000.000000	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	16000.000000	16000.000000	1.600000e+04	16000.000000	16000.000000	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	16000.000000	16000.000000	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	16000.000000	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	16000.000000	16000.000000	16000.000000	16000.000000	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	1.600000e+04	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.0	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.00000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000	16000.000000
mean	1.667694e-16	-8.243406e-17	0.261000	-2.559480e-16	-8.903989e-17	2.926548e-16	-2.555595e-17	-4.130030e-17	4.838838e-17	-3.533851e-14	-2.584443e-16	3.158099e-16	-4.493956e-15	693.165000	697.165125	9.364731e-17	0.525750	0.210687	-2.892131e-17	-5.148312e-16	-3.172809e-17	-1.241507e-16	-2.401378e-17	0.739062	0.297938	7.833734e-16	-2.640110e-16	-9.863187e-16	5.351553e-16	-3.720635e-17	1.578598e-16	1.020677e-16	0.972625	-7.804174e-17	8.437695e-18	1.402697e-16	8.307331e-17	3.383210e-17	-6.251943e-17	-5.439260e-16	-6.276507e-16	6.811218e-17	-8.104975e-17	2.676678e-17	6.170064e-17	-9.912210e-17	4.843348e-17	3.394441e-15	7.420106e-17	0.987875	0.251688	0.922937	0.359812	-4.464901e-16	3.790787e-16	7.372228e-17	-4.350340e-16	1.106060e-17	2.710939e-16	1.001560e-16	1.829890e-16	-1.922783e-15	6.281260e-17	-2.092170e-15	3.892719e-17	-9.751215e-16	-9.632573e-17	-9.729093e-16	9.517283e-16	1.691425e-15	7.864820e-16	-1.955325e-15	6.443058e-16	0.464062	0.000063	0.121250	0.413562	0.414563	0.316000	0.195500	0.577625	0.072938	0.004938	0.023875	0.014875	0.009062	0.069125	0.000812	0.013375	0.008563	0.0	0.011875	0.008937	0.024438	0.143750	0.020438	0.014063	0.002063	0.003375	0.078375	0.032188	0.005687	0.003563	0.035937	0.018062	0.008125	0.009500	0.011750	0.023187	0.023438	0.002875	0.024750	0.018500	0.014313	0.005750	0.00275	0.030187	0.002500	0.005188	0.003812	0.034125	0.005625	0.015187	0.079250	0.033188	0.008563	0.009938	0.033188	0.004500	0.010812	0.002000	0.017063	0.085125	0.007063	0.025437	0.001813	0.019125	0.013062	0.001250	0.001500
std	1.000031e+00	1.000031e+00	0.439194	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	31.093148	31.093760	1.000031e+00	0.499352	0.407810	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	0.439160	0.457366	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	0.163179	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	0.109447	0.433996	0.266699	0.479960	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	1.000031e+00	0.498722	0.007906	0.326428	0.492487	0.492662	0.464927	0.396598	0.493953	0.260042	0.070096	0.152664	0.121056	0.094768	0.253675	0.028494	0.114878	0.092140	0.0	0.108327	0.094118	0.154408	0.350847	0.141496	0.117752	0.045369	0.057998	0.268769	0.176503	0.075203	0.059582	0.186140	0.133182	0.089775	0.097007	0.107762	0.150503	0.151293	0.053544	0.155367	0.134755	0.118779	0.075613	0.05237	0.171108	0.049939	0.071839	0.061630	0.181556	0.074791	0.122302	0.270137	0.179132	0.092140	0.099194	0.179132	0.066933	0.103423	0.044678	0.129508	0.279076	0.083744	0.157455	0.042536	0.136969	0.113546	0.035334	0.038702
min	-5.668754e+00	-1.506594e+00	0.000000	-1.660629e+00	-1.497147e+00	-1.517425e+00	-1.814905e+00	-1.432943e+00	-9.979486e-01	-6.778308e-01	-1.909580e+00	-3.763290e-01	-5.651713e+00	660.000000	664.000000	-7.091658e-01	0.000000	0.000000	-1.898702e+00	-3.977398e-01	-7.186158e-01	-1.999024e+00	-1.879410e+00	0.000000	0.000000	-1.708697e-01	-1.754900e-01	-7.584839e-02	-8.890935e-01	-9.382181e-01	-8.193343e-01	-1.056480e+00	0.000000	-8.641200e-01	-2.008902e+00	-9.205974e-01	-1.141842e+00	-1.047570e+00	-2.921467e+00	-7.002819e-01	-5.711767e-01	-9.439492e-01	-1.522478e+00	-8.340071e-01	-6.903186e-01	-1.913551e+00	-8.331548e-02	-2.507871e-02	-8.131511e-01	0.000000	0.000000	0.000000	0.000000	-3.939124e-01	-1.566318e+00	-1.561550e+00	-1.638997e+00	-1.183329e+00	-1.757034e+00	-1.489415e+00	-1.679084e+00	-2.500782e-02	-6.425621e-02	-1.868231e-01	-1.214740e+00	-8.624141e+00	-1.137415e+00	-4.018758e-01	-1.453849e-01	-1.068134e-01	-9.320399e-02	-1.259621e-01	-9.015560e-02	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	-8.741340e-01	-7.444671e-01	0.000000	-6.837144e-01	-7.229014e-01	-7.716013e-01	-7.704628e-01	-9.128776e-01	-4.012448e-01	-6.778308e-01	-6.748075e-01	-3.763290e-01	-5.357199e-01	670.000000	674.000000	-7.091658e-01	0.000000	0.000000	-6.755860e-01	-3.977398e-01	-4.622053e-01	-7.626065e-01	-7.198970e-01	0.000000	0.000000	-1.708697e-01	-1.754900e-01	-7.584839e-02	-8.890935e-01	-6.092063e-01	-8.193343e-01	-4.801920e-01	1.000000	-6.133488e-01	-4.374109e-01	-9.205974e-01	-7.865363e-01	-6.240137e-01	-6.468691e-01	-7.002819e-01	-5.711767e-01	-5.623436e-01	-6.644114e-01	-6.366822e-01	-5.828766e-01	-7.887079e-01	-8.331548e-02	-2.507871e-02	-8.131511e-01	1.000000	0.000000	1.000000	0.000000	-3.939124e-01	-6.987472e-01	-5.774057e-01	-7.815426e-01	-6.480487e-01	-7.034194e-01	-7.538447e-01	-7.730932e-01	-2.500782e-02	-6.425621e-02	-1.868231e-01	-7.239486e-01	-3.181981e-01	-1.137415e+00	-4.018758e-01	-1.453849e-01	-1.068134e-01	-9.320399e-02	-1.259621e-01	-9.015560e-02	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	-1.056832e-01	-2.640553e-01	0.000000	-1.727680e-01	-2.662447e-01	-2.577753e-02	-2.443249e-02	-1.327792e-01	-1.547802e-01	-6.778308e-01	-6.776001e-02	-3.763290e-01	2.513560e-01	685.000000	689.000000	-7.091658e-01	1.000000	0.000000	-1.513934e-01	-3.977398e-01	-2.393893e-01	-1.264820e-02	-1.401406e-01	1.000000	0.000000	-1.708697e-01	-1.754900e-01	-7.584839e-02	-9.044675e-02	-2.801946e-01	1.602180e-01	-4.801920e-01	1.000000	-2.602490e-01	2.854752e-01	-3.053276e-01	-7.592443e-02	-2.616205e-01	7.906649e-02	-9.482905e-02	-5.711767e-01	-1.807380e-01	-9.236726e-02	-3.980944e-01	-3.480124e-01	7.446455e-02	-8.331548e-02	-2.507871e-02	-2.600106e-01	1.000000	0.000000	1.000000	0.000000	-3.939124e-01	-2.649617e-01	-2.493576e-01	-1.384520e-01	-2.465885e-01	-2.819736e-01	-1.408694e-01	-1.690994e-01	-2.500782e-02	-6.425621e-02	-1.868231e-01	-2.331568e-01	3.748806e-01	-2.190845e-01	-4.018758e-01	-1.453849e-01	-1.068134e-01	-9.320399e-02	-1.259621e-01	-9.015560e-02	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	5.962913e-01	5.620352e-01	1.000000	6.188390e-01	5.424971e-01	7.200462e-01	5.723918e-01	1.167385e+00	1.695154e-01	1.475295e+00	6.091988e-01	-3.763290e-01	6.448939e-01	710.000000	714.000000	3.895708e-01	1.000000	0.000000	5.475301e-01	-3.977398e-01	1.338768e-01	7.494716e-01	5.224382e-01	1.000000	1.000000	-1.708697e-01	-1.754900e-01	-7.584839e-02	7.082000e-01	4.881711e-02	1.602180e-01	6.723841e-01	1.000000	2.593788e-01	7.254928e-01	3.099421e-01	2.793815e-01	3.093317e-01	7.082107e-01	5.106238e-01	1.278043e-01	2.008677e-01	4.796769e-01	3.257373e-01	1.628211e-01	8.730690e-01	-8.331548e-02	-2.507871e-02	2.931299e-01	1.000000	1.000000	1.000000	1.000000	2.999771e-01	6.026095e-01	4.067387e-01	5.046385e-01	4.225117e-01	5.609180e-01	4.721059e-01	4.348944e-01	-2.500782e-02	-6.425621e-02	-1.868231e-01	2.576350e-01	6.829156e-01	7.020041e-01	-4.018758e-01	-1.453849e-01	-1.068134e-01	-9.320399e-02	-1.259621e-01	-9.015560e-02	1.000000	0.000000	0.000000	1.000000	1.000000	1.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	2.307412e+00	2.739539e+00	1.000000	2.957678e+00	3.759527e+00	2.957517e+00	3.258101e+00	1.167385e+00	8.582273e+01	1.475295e+00	2.881401e+01	1.663589e+01	1.825508e+00	845.000000	850.000000	4.784517e+00	1.000000	1.000000	1.033246e+01	4.094505e+01	2.683657e+01	4.337110e+00	5.905890e+00	1.000000	1.000000	1.960582e+01	1.087271e+01	4.250570e+01	1.029196e+01	1.090620e+01	9.955741e+00	1.046928e+01	1.000000	1.494211e+01	3.554178e+00	1.138480e+01	1.484692e+01	1.846216e+01	4.483076e+00	1.261968e+01	1.620437e+01	1.012261e+01	1.249260e+01	1.268213e+01	1.489108e+01	4.281581e+00	3.694579e+01	6.607522e+01	8.590238e+00	1.000000	1.000000	1.000000	1.000000	2.319833e+01	8.410750e+00	1.024818e+01	6.506817e+00	9.522276e+00	1.109706e+01	6.847049e+00	8.890807e+00	3.998750e+01	2.810285e+01	2.776801e+01	1.007347e+01	6.829156e-01	1.620335e+00	1.178541e+01	5.779400e+01	3.432827e+01	2.617172e+01	1.225695e+01	1.109193e+01	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000

	state_codes	issued_loans	interest_rate	annual_income
0	AK	12491300.0	13.53	80730.84
1	AL	55503475.0	14.13	71242.99
2	AR	33473650.0	13.92	69699.80
3	AZ	122958650.0	13.46	75824.19
4	CA	725545250.0	13.54	87677.63

	state_codes	issued_loans	interest_rate	annual_income
4	CA	725545250.0	13.54	87677.63
9	FL	342036825.0	13.82	74264.63
33	NY	377734975.0	13.89	82417.00
42	TX	427782800.0	13.57	83526.73

loan_status	Charged Off	Fully Paid	Fully Paid %
int_round
5	504	11810	95.907098
6	276	3428	92.548596
7	1538	18841	92.453015
8	2702	20635	88.421819
9	2412	15833	86.779940
10	3049	18015	85.525066
11	8184	34952	81.027448
12	2559	9219	78.273051
13	7896	25552	76.393207
14	8627	23980	73.542491
15	4140	10870	72.418388
16	5474	13444	71.064595
17	4029	7806	65.956907
18	3900	7656	66.251298
19	3805	6705	63.796384
20	3760	5870	60.955348
21	2979	4562	60.495955
22	1714	1976	53.550136
23	1404	1995	58.693733
24	1891	2550	57.419500
25	2242	2901	56.406766
26	1882	2428	56.334107
27	763	561	42.371601
28	463	355	43.398533
29	894	954	51.623377
30	677	785	53.693570
31	1229	1433	53.831705

	id	loan_amnt	funded_amnt	funded_amnt_inv	term	int_rate	installment	grade	sub_grade	emp_length	home_ownership	annual_inc	verification_status	issue_d	loan_status	purpose	zip_code	addr_state	dti	delinq_2yrs	earliest_cr_line	fico_range_low	fico_range_high	inq_last_6mths	mths_since_last_delinq	open_acc	revol_bal	revol_util	total_acc	initial_list_status	open_acc_6m	open_act_il	open_il_12m	open_il_24m	mths_since_rcnt_il	total_bal_il	il_util	open_rv_12m	open_rv_24m	max_bal_bc	all_util	inq_fi	total_cu_tl	inq_last_12m	acc_open_past_24mths	avg_cur_bal	bc_open_to_buy	bc_util	mo_sin_old_il_acct	mo_sin_old_rev_tl_op	mo_sin_rcnt_rev_tl_op	mo_sin_rcnt_tl	mort_acc	mths_since_recent_bc	mths_since_recent_inq	num_actv_bc_tl	num_actv_rev_tl	num_bc_sats	num_bc_tl	num_il_tl	num_op_rev_tl	num_rev_accts	num_rev_tl_bal_gt_0	num_sats	num_tl_op_past_12m	pct_tl_nvr_dlq	percent_bc_gt_75
0	102675947	14000.0	14000.0	14000.0	1	15.99	340.38	C	C5	10.0	RENT	43000.0	Source Verified	2017	Charged Off	debt_consolidation	367xx	AL	21.80	1.0	1995	670.0	674.0	0.0	1	3.0	18537.0	99.1	8.0	1	0.0	1.0	0.0	0.0	1	8035.0	42.0	0.0	0.0	18537.0	70.0	0.0	0.0	0.0	0.0	8857.0	163.0	99.1	1	1	1	1	0.0	1	0	1.0	1.0	1.0	2.0	5.0	1.0	3.0	1.0	3.0	0.0	87.5	100.0
1	104220223	5000.0	5000.0	5000.0	0	14.99	173.31	C	C4	10.0	RENT	68000.0	Not Verified	2017	Fully Paid	debt_consolidation	945xx	CA	22.50	0.0	2003	660.0	664.0	0.0	1	6.0	10276.0	90.1	18.0	0	0.0	2.0	0.0	2.0	1	25892.0	64.0	0.0	0.0	4261.0	69.0	1.0	1.0	0.0	2.0	6028.0	1124.0	90.1	1	1	1	1	0.0	1	1	4.0	4.0	4.0	6.0	8.0	4.0	9.0	4.0	6.0	0.0	94.4	75.0
2	104090118	10150.0	10150.0	10150.0	0	7.24	314.52	A	A3	8.0	MORTGAGE	50000.0	Not Verified	2017	Fully Paid	debt_consolidation	773xx	TX	29.60	0.0	2002	740.0	744.0	1.0	0	9.0	21845.0	56.0	21.0	1	1.0	3.0	1.0	1.0	1	23502.0	43.0	0.0	0.0	11270.0	49.0	1.0	1.0	2.0	2.0	29908.0	13951.0	58.2	1	1	1	1	3.0	1	1	3.0	4.0	3.0	5.0	8.0	5.0	10.0	4.0	9.0	2.0	100.0	33.3
3	103739310	8400.0	8400.0	8400.0	0	11.39	276.56	B	B3	8.0	MORTGAGE	50000.0	Source Verified	2017	Charged Off	other	454xx	OH	15.63	0.0	2005	675.0	679.0	0.0	0	14.0	12831.0	30.3	30.0	1	3.0	2.0	1.0	2.0	1	38760.0	105.0	4.0	8.0	5338.0	65.0	4.0	1.0	7.0	10.0	12389.0	24145.0	33.1	1	1	1	1	4.0	1	1	4.0	5.0	7.0	11.0	9.0	11.0	16.0	5.0	14.0	5.0	100.0	14.3
4	104046532	10000.0	10000.0	10000.0	0	12.74	335.69	C	C1	10.0	OWN	40000.0	Not Verified	2017	Fully Paid	debt_consolidation	324xx	FL	8.85	0.0	1997	700.0	704.0	0.0	0	7.0	9227.0	55.9	15.0	0	0.0	0.0	0.0	0.0	1	0.0	0.0	1.0	3.0	5454.0	56.0	0.0	0.0	1.0	3.0	1318.0	1691.0	79.4	1	1	1	1	2.0	1	1	2.0	4.0	2.0	3.0	2.0	7.0	11.0	4.0	7.0	1.0	100.0	50.0

	id	loan_amnt	term	int_rate	installment	grade	sub_grade	emp_length	annual_inc	issue_d	dti	delinq_2yrs	earliest_cr_line	fico_range_low	fico_range_high	inq_last_6mths	mths_since_last_delinq	mths_since_last_record	open_acc	pub_rec	revol_bal	revol_util	total_acc	initial_list_status	mths_since_last_major_derog	annual_inc_joint	dti_joint	acc_now_delinq	open_acc_6m	open_act_il	open_il_12m	open_il_24m	mths_since_rcnt_il	total_bal_il	il_util	open_rv_12m	open_rv_24m	max_bal_bc	all_util	inq_fi	total_cu_tl	inq_last_12m	acc_open_past_24mths	avg_cur_bal	bc_open_to_buy	bc_util	chargeoff_within_12_mths	delinq_amnt	mort_acc	mths_since_recent_bc	mths_since_recent_bc_dlq	mths_since_recent_inq	mths_since_recent_revol_delinq	num_accts_ever_120_pd	num_actv_bc_tl	num_bc_sats	num_bc_tl	num_il_tl	num_op_rev_tl	num_rev_accts	num_rev_tl_bal_gt_0	num_tl_120dpd_2m	num_tl_30dpd	num_tl_90g_dpd_24m	num_tl_op_past_12m	pct_tl_nvr_dlq	percent_bc_gt_75	pub_rec_bankruptcies	tax_liens	revol_bal_joint	sec_app_mort_acc	sec_app_revol_util	sec_app_mths_since_last_major_derog	home_MORTGAGE	home_NONE	home_OWN	home_RENT	verify_Source Verified	verify_Verified	purp_credit_card	purp_debt_consolidation	purp_home_improvement	purp_house	purp_major_purchase	purp_medical	purp_moving	purp_other	purp_renewable_energy	purp_small_business	purp_vacation	purp_wedding	state_AL	state_AR	state_AZ	state_CA	state_CO	state_CT	state_DC	state_DE	state_FL	state_GA	state_HI	state_ID	state_IL	state_IN	state_KS	state_KY	state_LA	state_MA	state_MD	state_ME	state_MI	state_MN	state_MO	state_MS	state_MT	state_NC	state_ND	state_NE	state_NH	state_NJ	state_NM	state_NV	state_NY	state_OH	state_OK	state_OR	state_PA	state_RI	state_SC	state_SD	state_TN	state_TX	state_UT	state_VA	state_VT	state_WA	state_WI	state_WV	state_WY
count	2.000000e+04	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	2.000000e+04	20000.000000	20000.000000	20000.000000	20000.000000	20000.00000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.00000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.00000	20000.000000	20000.0000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.00000	20000.000000	20000.000000	20000.00000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.00000	20000.000000	20000.000000	20000.000000	20000.0	20000.000000	20000.000000	20000.000000	20000.000000	20000.00000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.00000	20000.000000	20000.00000	20000.000000	20000.000000	20000.000000	20000.000000	20000.00000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.000000	20000.00000	20000.00000
mean	8.995035e+07	14845.695000	0.262550	14.527035	459.439889	3.029100	13.132300	5.504275	7.711921e+04	2016.316400	19.428785	0.354800	2000.068650	693.31600	697.316150	0.647250	0.527850	0.210200	11.869650	0.266850	15585.475900	49.242785	24.716200	0.740250	0.299300	3802.495215	0.688934	0.007000	1.108800	2.856100	0.837950	1.836950	0.973600	35986.670600	63.984300	1.492350	3.204150	5390.908650	60.317750	1.150700	1.649400	2.472950	5.317800	12935.132350	10274.992550	56.271810	0.008900	22.014000	1.479250	0.988350	0.25425	0.923100	0.360900	0.568700	3.609100	4.767800	7.636500	8.88310	8.331300	14.1224	5.547100	0.000750	0.004350	0.094450	2.472500	93.738685	41.245695	0.165000	0.065550	593.181250	0.032750	1.173845	0.008250	0.462800	0.000050	0.123400	0.41280	0.414300	0.317100	0.19315	0.580150	0.073500	0.005400	0.024150	0.014450	0.009050	0.06930	0.000850	0.012700	0.007900	0.0	0.011600	0.008550	0.025000	0.142600	0.02075	0.013850	0.002050	0.003550	0.078100	0.031900	0.00550	0.003500	0.03630	0.018050	0.007700	0.009350	0.011400	0.02325	0.022850	0.002850	0.025000	0.018150	0.014600	0.006150	0.002550	0.030300	0.002450	0.005450	0.003950	0.034750	0.006600	0.014900	0.080750	0.032050	0.009400	0.010200	0.033100	0.004300	0.010750	0.002250	0.016350	0.084300	0.007500	0.025750	0.001850	0.019450	0.013000	0.00155	0.00155
std	1.582901e+07	9175.396697	0.440031	5.564589	285.921531	1.344263	6.717764	3.848871	7.514298e+04	0.465083	11.036975	0.949717	7.652501	31.21859	31.219321	0.912228	0.499236	0.407461	5.732146	0.661712	21401.815729	24.738491	12.108133	0.438508	0.457963	21979.555941	3.892103	0.092474	1.242191	3.026838	1.020462	1.722386	0.160326	41740.899612	31.802208	1.625753	2.823521	5678.146806	20.640692	1.648886	2.858179	2.629261	3.487436	15427.656235	14876.425265	29.491357	0.105931	890.362325	1.814479	0.107307	0.43545	0.266439	0.480274	1.449547	2.318095	3.077362	4.673239	7.52271	4.768383	8.1751	3.325122	0.027377	0.069508	0.519849	2.030751	9.114103	36.310992	0.411198	0.446389	5510.168982	0.347683	9.105016	0.090456	0.498627	0.007071	0.328904	0.49235	0.492613	0.465358	0.39478	0.493547	0.260962	0.073288	0.153519	0.119339	0.094702	0.25397	0.029143	0.111979	0.088532	0.0	0.107079	0.092072	0.156129	0.349673	0.14255	0.116871	0.045232	0.059478	0.268336	0.175738	0.07396	0.059059	0.18704	0.133136	0.087413	0.096245	0.106163	0.15070	0.149429	0.053311	0.156129	0.133497	0.119948	0.078182	0.050434	0.171416	0.049438	0.073625	0.062726	0.183151	0.080974	0.121156	0.272458	0.176137	0.096499	0.100481	0.178902	0.065435	0.103126	0.047382	0.126821	0.277844	0.086279	0.158393	0.042973	0.138104	0.113277	0.03934	0.03934
min	6.510400e+04	1000.000000	0.000000	5.320000	30.650000	1.000000	1.000000	0.000000	0.000000e+00	2016.000000	0.000000	0.000000	1956.000000	660.00000	664.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	2.0000	0.000000	0.000000	0.000000	0.000000	0.000000	15.400000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.00000
25%	7.605364e+07	8000.000000	0.000000	10.750000	252.542500	2.000000	8.000000	2.000000	4.600000e+04	2016.000000	12.520000	0.000000	1996.000000	670.00000	674.000000	0.000000	0.000000	0.000000	8.000000	0.000000	5537.750000	30.300000	16.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	1.000000	1.000000	10432.500000	50.000000	0.000000	1.000000	2167.750000	47.000000	0.000000	0.000000	1.000000	3.000000	3052.750000	1601.750000	32.900000	0.000000	0.000000	0.000000	1.000000	0.00000	1.000000	0.000000	0.000000	2.000000	3.000000	4.000000	4.00000	5.000000	8.0000	3.000000	0.000000	0.000000	0.000000	1.000000	90.900000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.00000
50%	8.821778e+07	12500.000000	0.000000	13.590000	382.680000	3.000000	12.000000	5.000000	6.500000e+04	2016.000000	18.670000	0.000000	2002.000000	685.00000	689.000000	0.000000	1.000000	0.000000	11.000000	0.000000	10414.500000	49.000000	23.000000	1.000000	0.000000	0.000000	0.000000	0.000000	1.000000	2.000000	1.000000	1.000000	1.000000	24998.500000	73.000000	1.000000	3.000000	4029.500000	62.000000	1.000000	0.000000	2.000000	5.000000	6801.500000	5053.500000	58.500000	0.000000	0.000000	1.000000	1.000000	0.00000	1.000000	0.000000	0.000000	3.000000	4.000000	7.000000	7.00000	7.000000	12.0000	5.000000	0.000000	0.000000	0.000000	2.000000	97.100000	33.300000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.00000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.00000
75%	9.956666e+07	20000.000000	1.000000	17.990000	612.942500	4.000000	17.000000	10.000000	9.100000e+04	2017.000000	25.540000	0.000000	2005.000000	710.00000	714.000000	1.000000	1.000000	0.000000	15.000000	0.000000	18528.250000	67.800000	31.250000	1.000000	1.000000	0.000000	0.000000	0.000000	2.000000	3.000000	1.000000	3.000000	1.000000	46629.250000	87.000000	2.000000	4.000000	6968.000000	75.000000	2.000000	2.000000	3.000000	7.000000	17941.750000	12665.500000	82.100000	0.000000	0.000000	2.000000	1.000000	1.00000	1.000000	1.000000	1.000000	5.000000	6.000000	10.000000	12.00000	11.000000	18.0000	7.000000	0.000000	0.000000	0.000000	3.000000	100.000000	66.700000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	1.00000	1.000000	1.000000	0.00000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.00000
max	1.264077e+08	40000.000000	1.000000	30.990000	1569.110000	7.000000	35.000000	10.000000	6.693021e+06	2017.000000	490.070000	17.000000	2014.000000	845.00000	850.000000	5.000000	1.000000	1.000000	71.000000	28.000000	598769.000000	162.000000	109.000000	1.000000	1.000000	434000.000000	61.280000	4.000000	14.000000	36.000000	11.000000	20.000000	1.000000	655706.000000	268.000000	20.000000	45.000000	361299.000000	162.000000	28.000000	48.000000	29.000000	49.000000	208984.000000	231418.000000	182.300000	4.000000	65000.000000	17.000000	1.000000	1.00000	1.000000	1.000000	34.000000	23.000000	36.000000	51.000000	80.00000	61.000000	92.0000	36.000000	1.000000	2.000000	16.000000	23.000000	100.000000	100.000000	5.000000	27.000000	184999.000000	9.000000	110.700000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	1.00000

loan_status	Charged Off	Fully Paid	Charged Off %
purpose
car	662	3228	17.018
credit_card	13753	50928	21.2628
debt_consolidation	47602	144826	24.7376
home_improvement	5138	20460	20.0719
house	470	1575	22.9829
major_purchase	1856	6441	22.3695
medical	1211	3419	26.1555
moving	766	2036	27.3376
other	5509	17519	23.9231
renewable_energy	73	184	28.4047
small_business	1331	2251	37.158
vacation	622	2248	21.6725
wedding	0	1	0

grade	A	B	C	D	E	F	G
loan_status
Charged Off	0.207161	0.380218	0.528376	0.633006	0.702936	0.793609	0.774648
Fully Paid	0.792839	0.619782	0.471624	0.366994	0.297064	0.206391	0.225352

loan_status	Charged Off	Fully Paid	Charged Off %
addr_state
AR	734	1802	28.943218
LA	1063	2672	28.460509
MS	572	1451	28.274839
OK	861	2192	28.201769
AL	1139	2971	27.712895
NY	7005	18971	26.967200
NJ	2894	8350	25.738171
NE	430	1244	25.686977
MD	1938	5618	25.648491
AK	188	553	25.371120
FL	6171	18528	24.984817
MO	1269	3872	24.683914
KY	818	2496	24.683162
OH	2659	8146	24.608977
NV	1341	4109	24.605505
IN	1447	4446	24.554556
PA	2636	8113	24.523211
NM	441	1386	24.137931
TX	6767	21442	23.988798
CA	11416	36307	23.921380
MN	1412	4497	23.895752
MI	2128	6786	23.872560
VA	2108	6796	23.674753

	sub_grade	int_rate
0	A1	5.320000
1	A2	6.769053
2	A3	7.152234
3	A4	7.483164
4	A5	8.034195